International Journal of Current Research and Review
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IJCRR - 13(15), August, 2021

Pages: 51-57

Date of Publication: 10-Aug-2021

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A Review on Esophageal Cancer Detection and Classification Using Deep Learning Techniques

Author: Kumar C A, Mubarak M N D

Category: Healthcare

Abstract:Introduction: Esophageal cancer (EC)is the sixth most common cancer with a high fatality rate. Early prognosis can improve the survival rate of the patients. The sequence of the progress of the EC is from Esophagitis to Non-Dysplasia Barrett's Esophagus to Dysplasia Barrett's Esophagus to Esophageal Adenocarcinoma (EAC). Computer-Aided Diagnosis (CAD) has become a primary tool of the decade to diagnose various diseases. Objective: The recent advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have enriched the potential of detection, localization, and classification of the medical image pattern. Here, we have compiled multiple research works based on supervised DL architectures. Methods: This review focuses on the application of DL techniques for the detection, segmentation, and classification of the various stages leading to the EAC. The surveyor concentrates on the pre-trained classification detection and segmentation models. Results: The advancements in AI have enhanced the contributions in the medical field applications. The technological progress in AI and DL led to a large number of researches in the medical field. The new algorithms and DL models resulted in many automated systems for the detection segmentation and classification of oesophagal cancer. Conclusion: This review discusses the various challenges, limitations, and future aspects of analysing endoscopic images based on DL methods. Further investigations are to be carried out to improve the performance of CAD systems for successful real-time detection of oesophagal and associated stages. It is essential to formulate more collaborated studies with experts in the field.

Keywords: Barrett’s Esophagus, Computer-Aided Diagnosis, Convolution Neural Networks, Deep Learning, Esophageal Adenocarcinoma, Machine Learning

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Acid reflux disease (GERD) is a condition that causes acidity and indigestion.1 The stomach acid's backwash will gradually replace the healthy oesophagal tissue with the tissue that resembles the intestinal/gastric tissues. Figure:1 shows the EC progression as a continuum variation from Esophagitis to Non-Dysplasia Barrett's Esophagus to Dysplasia Barrett's Esophagus to EAC. The change in the texture of the oesophagal tissue lining in Barrett’s Esophagus (BE)may lead to dysplasia. Based on the variation in the texture pattern of the oesophagus lining, the abnormal tissue cells can be categorized as non-dysplastic (noncancerous), High-grade dysplasia (HGD), and Low-grade dysplasia (LGD).2 The condition of BE with High-grade dysplasia is an advanced pre-cancer stage leading to EAC. and EAC's occurrence has drastically increased in Western countries, especially in industrial countries. One of the main reasons for the drastic increase in the rate is due to society's unhealthy lifestyles. The sixth most common cancer with a high fatality rate is EC3. An early prognosis can improve the survival rate of the patient ( Fig 1).

CAD is one of the most prominent tools for providing an improved prognosis for EC. In recent years AI has made remarkable progress in the medical domain. The deep neural network's ability to automatically learn significant low-level features and combine them with high-level features enhances DL performance in medical applications.4 DL with CNN is one of the most efficient and standard tools to perform multiple tasks like object recognition, classification, and segmentation.3-6 We aim to review and study the feasibility of DL for a CAD system using CNN for the prognosis of premalignant and malignant stages of BE (Fig 2).


AI is an area of rapid technological advancement, especially in medical applications. AI equips the computer to emulate human intelligence5. Both Machine Learning (ML) and DL are a subset of AI. The DL technique addressed all the major concepts involved in image processing.6 The DL structure has deep CNN networks with a significant number of hidden layers as shown in Figure:2. In health care applications, the DL is considered state-of-the-art technology by its performance.7 Figure:3 depicts the supervised DL models for medical image processing.

Classification Models

       The input image traverse through a set of convolutional kernels with down-sampling. A softmax layer towards the end of the model will generate the class probability for classification.8 The model performs the classification process based on the category, subcategory, lesion-based analysis, and lesion morphology like invasion depth. The various classification models are AlexNet,9 VGG,10 GoogLeNet,11 Inception V4,12 Residual Network (ResNet)13and DenseNet.14

 Segmentation Models

            The segmentation process is the delineation of the lesions' borders under consideration15. Generally, all the segmentation models have two modules, an encoder and a decoder8. The encoder performs the convolution of the image with kernels and downsamples the feature maps. The decoder module performs the deconvolution and the up-sampling process. The model performs both the Semantic and Instance segmentation. Some of the popular segmentation models are Fully Convolution Network (FCN),16 SegNet,17 U-Net,18 Deep Lab,19 DeepLabV3.20

Detection Models

The complete knowledge of an image is conceived when we accurately understand the object's nature and location in the picture.21 The object detection model needs to establish the object location (localization) and the category of the object(classification). A deeper analysis needs to be done by the model to predict the subcategory of the objects. The detection will be based on the object scores obtained from the feature extracted by the fully connected layers in some models.  Multiple object detection can also be performed based on the class and object score derived from the feature extracted.  The R-CNN,22 Fast R-CNN,23 Faster R-CNN,24 Mask R-CNN,25 Single Shot Multi-BoxDetector (SSD)26 and YOLO27are some of the prevalent detection models used. 


Takiyama et al.28 propose a GoogLeNet model trained by a back-propagation algorithm for the organ's anatomical classification, larynx, oesophagus, stomach, and duodenum. The network is fine-tuned with an ADAM optimizer with a learning rate of 0.0002.

Kumagai et al.29proposes a DL model for the classification of ESCC. Endocytoscopy(ECS) is a magnified endoscopic method that enables the observation of surface epithelial cells in real-time. The ECS enables a real-time optical biopsy. The ECS performs tissue analysis and perceives the histological features in real-time. The diagnosis can be made without performing biopsy reference of the histology using GoogLeNet trained by a back-propagation algorithm. Ada Delta fine-tunes the training of the model with a learning rate of 0.004.

In Tokai et al.30 the ESCC detection is performed with SSD at a rate of 95.7% in 10sec. The GoogLeNet evaluated the measure of invasion depth of ESCC and the sub-classification for WLI and NBI. The network was trained with all the CNN layers fine-tuned by stochastic gradient descent (SGDM) with a learning rate of 0.0001.

Van der Putten et al.31 proposes a combination of multiple modalities to analyze early neoplasia in BE for better localization accuracy. The sweet spot and soft spot is predefined, and the image registration technique is applied for aligning BLI to WLI. The Canny edge detection is applied for enhancing image pairs.Resnet18 is used for the classification of the patches, which is fine-tuned with an ADAM optimizer.

Tomita et al.32proposes an attention-based model with Resnet18 for the tissue level classification of the histological image into normal, BE with no dysplasia, BE with dysplasia, and EAC. Grid-based feature extraction performed with Resnet18, followed by a 3D CNN kernel to build an attention map. The attention map with the attention feature weights is combined to obtain the feature vector for the tissue classification. The model is trained with high-resolution images and fine-tuned with an ADAM optimizer.

Liu et al. 33proposes an Inception- ResNet model to classify premalignant cancer and EC lesions. The original and preprocessed image is applied as input to the Inception -ResNet model separately for feature extraction. The features obtained are concatenated through a concatenation fusion function. The model achieved better performance when trained and fine-tuned with SGDM optimization with a learning rate of 0.001.

Ebigbo et al. 34 proposed a ResNet network for BE analysis. The pathologically validated images served as a reference for classification. The images labelled by experts serve as the standard of reference for the segmentation process. All validation was carried out using Leave-One-Patient-Out Cross-validation (LOpOCV). The small patches are extracted from the endoscopic colour images, and augmentation is applied to obtain diversified images of a similar class. The classification of the full image was attained by culminating the probability of each patch class.

Hong et al.35proposes a simple CNN architecture to differentiate between sub-classes IM, GM, and NPL. The proposed CNN yielded 80.77 per cent classification accuracy among sub-classes of IM, GM, and NPL. 

Van Riel et al.36proposes a new technique for achieving real-time performance in early EC diagnosis using CNN and transfer learning. Performance analysis of some of the popular pre-trained networks such as AlexNet, VGG'16, and GoogLeNet was evaluated using the knowledge transferred from the ImageNet dataset with classifiers such as Support Vector Machine and Random Forest individually. The window-based approach out-performed the existing methods and achieved an AUC of 0.92 (area under the curve).

Mendel et al.37proposes an automated CNN model for the early diagnosis of EAC from High-definition endoscopic images using transfer learning. The ResNet architecture was used to train the models and leave one patient out cross-validation (LOPO-CV) method for evaluation.

Rezvy et al.38focus on a modified Mask-RCNN to detect and segment the Precancerous, BE, High-GradeDysplasia (HGD), EAC and Polyps trained with feature representation in transfer learning mode. The network head of ResNet101 trained on the COCO dataset is used to replace the network head of the AI model. The new model is acquainted with the augmented dataset and fine-tuned with ADAM optimization with a learning rate of 0.0001 for the detection.

Gao et al.39 explore the viability of attaining synchronized processing of endoscopic video for determining the pre-cancer status of squamous cell cancer. The detection architectures, mask-RCNN, and YOLOv3 are used to detect and segment endoscopic images. The images are classified as SCC's 'cancer,' 'high risk,' and 'suspicious.' The Mask RCNN outperformed YOLOv3 both in generating the masks and classification accuracy. YOLOV3 processed the video frames at a rate of 0.1spf, which is ten times faster than Mask RCNN.

Hori et al.40 proposed an AI-based diagnostic system with SSD for diagnosing EAC and Squamous Cell Carcinoma (SCC). CNN precisely diagnosed all the cases of ECs from the combined analysis of WLI and NBI images. The diagnostic system was able to recognize even the smallest lesions (<10mm). CNN diagnostic model achieved a more enhanced performance with the comprehensive diagnosis of NBI and WLI images.

In Ghatwary et al.41, the study focuses on the evaluation of EAC regions from the high-definition white-light endoscopy (HD-WLE) images by using different DLobject detection methods. The SSD, Region-based Convolution Neural Network (R-CNN), Faster R-CNN, Fast R-CNN models explored in this study. From the experimental results, we can infer that the SSD outclass all other approaches. The pre-trained model VGG16 was used for better classification of the EAC.

Zhao et al.42proposes a double-labelled FCN model with multi-task learning for the detection and semantic segmentation of ESCC. The FCN, when the trained end to end within the self-transfer learning framework, optimizes the Region of Interest and Semantic Labels simultaneously. The double labelling FCN model can provide extra attributes for better pixel classification. The type A classification is made for those with inflammations. Type B1 and B2 show the invasion depth of ESSC, which can be treated by local resection, and type B3, the invasion depth is >200µm, which requires surgical treatment. The double-labelled FCN model has a better performance with improved diagnostic accuracy.

Liu et al.43proposes a DeepLabV3+ model for early EC detection and segmentation. DeepLabV3+ has an Xception architecture with Atrous Convolution (AC) and Atrous Spatial Pyramidal Pooling(ASPP). The encoder stage extracts in-depth features by applying AC, and then the feature map is applied to ASPP for generating multiscale features. The decoder stage refines the segmentation process. The feature from the AC in the encoder is simplified via a 1x1 CNN and concatenated with multiscale features. Again, these features are applied to a 3x3 CNN and up-sampled to generate a binary semantic segmentation. The binary segmented image is further processed using the morphological and hole filling process to obtain the annotated image.

Guo et al.44 explore a CAD SegNet model for real-time identification and segmentation of pre-cancerous lesions and early ESCC. SegNet is a deep encoder-decoder module for multiclass segmentation. The encoders extract the low-resolution features with the boundary information stored in the max-pooling indices. The decoders consist of a pixel-wise classifier. It up-samples the attribute maps using the max-pooling indices of the analogous encoder for generating sparse attribute maps. The up-sampling feature map obtained is then convolved to achieve the dense feature map. The dense feature map will reduce the number of parameters, so SegNet needs less memory space and requires less computational time.

Van der Putten. et al.45AI proposes a network Gastro Net model with multi-task learning is proposed to obtain better localization, classification, and semantic segmentation of the BE. A ResNet replaces the CNN network in the encoder and decoder path of the U-Net. The fully connected feature layer and classification layer is added to the bridge network to multi-task learning. Both the classification and segmentation processes perform simultaneously in a single training.  Pseudo Labeling, a semi-supervised learning algorithm with Bootstrapping and Ensemble learning, provides more suitable descriptive attributes for enabling multi-stage transfer learning. The model is trained and fine-tuned with ADAM and AMS grad with a weight decay of 10-5 with a cosine cyclic learning rate schedule.

Omura et al.46 focus on a DL model for early EAC detection using a four-layer neural network with the feature extraction performed by Dyadic Wavelet Transform (DYDWT). The input RGB image is converted into HSV and CIELa*b*.  A fusion image is constructed by normalizing the S, a*b* components. This fusion image undergoes a contrast enhancement to obtain Sa*bhist*. The new image undergoes a 3-level decomposition using DYDWT. The Inverse DYDWT is applied for image reconstruction. The reconstructed image is the input to the neural network for classification. The DYDWT reduces the input features resulting in faster learning and high computational speed.

Ghatwary et al.47proposed a DL method for the automatic detection and classification of oesophagal abnormalities. The local features extracted through the Gabor filters and the CNN(DenseNet) are concatenated for enhancing the detection of abnormal regions using Faster RCNN. The overview of the DL analysis of endoscopic images for the different stages of Esophageal Cancer is shown in Table :1.


The technological progress in AI and DL led to a large number of research in the medical field. The new algorithms and DL models resulted in many automated systems for the detection, segmentation, and classification of EC. CNN is currently the backbone of all the DLarchitectures.

Many new network models are based on the multi-dimensional arrangements of CNN layers.  Most of the currently used DL models are supervised learning type models. The researchers need to work on the semi-supervised and unsupervised models for the diversified analysis of EC. Studies need to be done on the combination of tasks and networks for better feature extraction. One of the significant drawbacks of medical image analysis is the lack of availability of medical data. The labelling or annotation of the available dataset using experts is a more challenging task. The data augmentation and annotation methods need to be experimented with to combat these challenges. The researchers must have a substantial public dataset of medical images only. A pre-trained network trained in the medical dataset provides more relevant attributes and better diagnostic accuracy through transfer learning.


The advancements in artificial intelligence and DL have contributed a lot to develop many CAD techniques for detecting EC in the early stage itself. This review concentrates on some of the recent studies on the diagnosis of different EAC stages with DLtechniques using CNN. More research works are to be carried out with the semi-supervised and unsupervised learning methods for the widespread analysis of the EC. With the invention of the deep generative models and application of hybrid networks, more evaluation and estimation of early oesophagal cancer features can be performed. One of the significant limitations is the lack of availability of data set in the medical fields. The DL approach using CNN and the transfer learning techniques can resolve this problem. Data augmentation seems to be a suitable solution to this unbalanced data availability. The development of many unsupervised network architectures is to be initiated in the future to overcome the dataset availability.


The authors acknowledge the immense help received from the scholars whose articles are cited and included in references to this manuscript. The authors are also grateful to authors/ editors/publishers of all those articles, journals, and books from which the literature for this article has been reviewed and discussed.

Conflict of Interest

The authors have no competing conflict of interest to declare.

Source of Funding

There is No funding obtained from any organization for carrying out this research work.

Author Contribution

CHEMPAK KUMAR. A - Conceptualization, Formal analysis, Visualization, Writing -original draft, Writing

-review & editing.

D.MUHAMMAD NOORUL MUBARAK- Writing -review & editing.


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Dr. Pramod Kumar Manjhi joined Editor-in-Chief since July 2021 onwards

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A Study by Monica K. entitled "Risk Predictors for Lymphoma Development in Sjogren Syndrome - A Systematic Review" is awarded Best Article for Vol 14 issue 13
A Study by Mokhtar M Sh et al. entitled "Prevalence of Hospital Mortality of Critically Ill Elderly Patients" is awarded Best Article for Vol 14 issue 12
A Study by Vidya S. Bhat et al. entitled "Effect of an Indigenous Cleanser on the Microbial Biofilm on Acrylic Denture Base - A Pilot Study" is awarded Best Article for Vol 14 issue 11
A Study by Pandya S. et al. entitled "Acute and 28-Day Repeated Dose Subacute Toxicological Evaluation of Coroprotect Tablet in Rodents" is awarded Best Article for Vol 14 issue 10
A Study by Muhammad Zaki et al. entitled "Effect of Hemoglobin Level on the Severity of Acute Bronchiolitis in Children: A Case-Control Study" is awarded Best Article for Vol 14 issue 09
A Study by Vinita S & Ayushi S entitled "Role of Colour Doppler and Transvaginal Sonography for diagnosis of endometrial pathology in women presenting with Abnormal Uterine Bleeding" is awarded Best Article for Vol 14 issue 08
A Study by Prabhu A et al. entitled "Awareness of Common Eye Conditions among the ASHA (Accredited Social Health Activist) Workers in the Rural Communities of Udupi District- A Pilot Study" is awarded Best Article for Vol 14 issue 07
A Study by Divya MP et al. entitled "Non-Echoplanar Diffusion-Weighted Imaging and 3D Fiesta Magnetic Resonance Imaging Sequences with High Resolution Computed Tomography Temporal Bone in Assessment and Predicting the Outcome of Chronic Suppurative Otitis Media with Cholesteatoma" is awarded Best Article for Vol 14 issue 06
A Study by Zahoor Illahi Soomro et al. entitled "Functional Outcomes of Fracture Distal Radius after Fixation with Two Different Plates: A Retrospective Comparative Study" is awarded Best Article for Vol 14 issue 05
A Study by Ajai KG & Athira KN entitled "Patients’ Gratification Towards Service Delivery Among Government Hospitals with Particular Orientation Towards Primary Health Centres" is awarded Best Article for Vol 14 issue 04
A Study by Mbungu Mulaila AP et al. entitled "Ovarian Pregnancy in Kindu City, D.R. Congo - A Case Report" is awarded Best Article for Vol 14 issue 03
A Study by Maryam MJ et al. entitled "Evaluation Serum Chemerin and Visfatin Levels with Rheumatoid Arthritis: Possible Diagnostic Biomarkers" is awarded Best Article for Vol 14 issue 02
A Study by Shanthan KR et al. entitled "Comparison of Ultrasound Guided Versus Nerve Stimulator Guided Technique of Supraclavicular Brachial Plexus Block in Patients Undergoing Upper Limb Surgeries" is awarded Best Article for Vol 14 issue 01
A Study by Amol Sanap et al. entitled "The Outcome of Coxofemoral Bypass Using Cemented Bipolar Hemiarthroplasty in the Treatment of Unstable Intertrochanteric Fracture of Femur in a Rural Setup" is awarded Best Article Award of Vol 13 issue 24
A Study by Manoj KP et al. entitled "A Randomized Comparative Clinical Trial to Know the Efficacy of Ultrasound-Guided Transversus Abdominis Plane Block Against Multimodal Analgesia for Postoperative Analgesia Following Caesarean Section" is awarded Best Article Award of Vol 13 issue 23
A Study by Karimova II et al. entitled "Changes in the Activity of Intestinal Carbohydrases in Alloxan-Induced Diabetic Rats and Their Correction with Prenalon" is awarded Best Article of Vol 13 issue 22
A Study by Ashish B Roge et al. entitled "Development, Validation of RP-HPLC Method and GC MS Analysis of Desloratadine HCL and It’s Degradation Products" is awarded Best Article of Vol 13 issue 21
A Study by Isha Gaurav et al. entitled "Association of ABO Blood Group with Oral Cancer and Precancer – A Case-control Study" is awarded Best Article for Vol 13 issue 20
A Study by Amr Y. Zakaria et al. entitled "Single Nucleotide Polymorphisms of ATP-Binding Cassette Gene(ABCC3 rs4793665) affect High Dose Methotrexate-Induced Nephrotoxicity in Children with Osteosarcoma" is awarded Best Article for Vol 13 issue 19
A Study by Kholis Ernawati et al. entitled "The Utilization of Mobile-Based Information Technology in the Management of Dengue Fever in the Community Year 2019-2020: Systematic Review" is awarded Best Article for Vol 13 issue 18
A Study by Bhat Asifa et al. entitled "Efficacy of Modified Carbapenem Inactivation Method for Carbapenemase Detection and Comparative Evaluation with Polymerase Chain Reaction for the Identification of Carbapenemase Producing Klebsiella pneumonia Isolates" is awarded Best Article for Vol 13 issue 17
A Study by Gupta R. et al. entitled "A Clinical Study of Paediatric Tracheostomy: Our Experience in a Tertiary Care Hospital in North India" is awarded Best Article for Vol 13 issue 16
A Study by Chandran Anand et al. entitled "A Prospective Study on Assessment of Quality of Life of Patients Receiving Sorafenib for Hepatocellular Carcinoma" is awarded Best article for Vol 13 issue 15
A Study by Rosa PS et al. entitled "Emotional State Due to the Covid – 19 Pandemic in People Residing in a Vulnerable Area in North Lima" is awarded Best Article for Vol 13 issue 14
A Study by Suvarna Sunder J et al. entitled "Endodontic Revascularization of Necrotic Permanent Anterior Tooth with Platelet Rich Fibrin, Platelet Rich Plasma, and Blood Clot - A Comparative Study" is awarded Best Article for Vol 13 issue 13
A Study by Mona Isam Eldin Osman et al. entitled "Psychological Impact and Risk Factors of Sexual Abuse on Sudanese Children in Khartoum State" is awarded Best Article for Vol 13 issue 12
A Study by Khaw Ming Sheng & Sathiapriya Ramiah entitled "Web Based Suicide Prevention Application for Patients Suffering from Depression" is awarded Best Article for Vol 13 issue 11
A Study by Purushottam S. G. et al. entitled "Development of Fenofibrate Solid Dispersions for the Plausible Aqueous Solubility Augmentation of this BCS Class-II Drug" is awarded Best article for Vol 13 issue 10
A Study by Kumar S. et al. entitled "A Study on Clinical Spectrum, Laboratory Profile, Complications and Outcome of Pediatric Scrub Typhus Patients Admitted to an Intensive Care Unit from a Tertiary Care Hospital from Eastern India" is awarded Best Article for Vol 13 issue 09
A Study by Mardhiah Kamaruddin et al. entitled "The Pattern of Creatinine Clearance in Gestational and Chronic Hypertension Women from the Third Trimester to 12 Weeks Postpartum" is awarded Best Article for Vol 13 issue 08
A Study by Sarmila G. B. et al. entitled "Study to Compare the Efficacy of Orally Administered Melatonin and Clonidine for Attenuation of Hemodynamic Response During Laryngoscopy and Endotracheal Intubation in Gastrointestinal Surgeries" is awarded Best Article for Vol 13 issue 07
A Study by M. Muthu Uma Maheswari et al. entitled "A Study on C-reactive Protein and Liver Function Tests in Laboratory RT-PCR Positive Covid-19 Patients in a Tertiary Care Centre – A Retrospective Study" is awarded Best Article of Vol 13 issue 06 Special issue Modern approaches for diagnosis of COVID-19 and current status of awareness
A Study by Gainneos PD et al. entitled "A Comparative Evaluation of the Levels of Salivary IgA in HIV Affected Children and the Children of the General Population within the Age Group of 9 – 12 Years – A Cross-Sectional Study" is awarded Best Article of Vol 13 issue 05 Special issue on Recent Advances in Dentistry for better Oral Health
A Study by Alkhansa Mahmoud et al. entitled "mRNA Expression of Somatostatin Receptors (1-5) in MCF7 and MDA-MB231 Breast Cancer Cells" is awarded Best Article of Vol 13 issue 06
A Study by Chen YY and Ghazali SRB entitled "Lifetime Trauma, posttraumatic stress disorder Symptoms and Early Adolescence Risk Factors for Poor Physical Health Outcome Among Malaysian Adolescents" is awarded Best Article of Vol 13 issue 04 Special issue on Current Updates in Plant Biology to Medicine to Healthcare Awareness in Malaysia
A Study by Kumari PM et al. entitled "Study to Evaluate the Adverse Drug Reactions in a Tertiary Care Teaching Hospital in Tamilnadu - A Cross-Sectional Study" is awarded Best Article for Vol 13 issue 05
A Study by Anu et al. entitled "Effectiveness of Cytological Scoring Systems for Evaluation of Breast Lesion Cytology with its Histopathological Correlation" is awarded Best Article of Vol 13 issue 04
A Study by Sharipov R. Kh. et al. entitled "Interaction of Correction of Lipid Peroxidation Disorders with Oxibral" is awarded Best Article of Vol 13 issue 03
A Study by Tarek Elwakil et al. entitled "Led Light Photobiomodulation Effect on Wound Healing Combined with Phenytoin in Mice Model" is awarded Best Article of Vol 13 issue 02
A Study by Mohita Ray et al. entitled "Accuracy of Intra-Operative Frozen Section Consultation of Gastrointestinal Biopsy Samples in Correlation with the Final Histopathological Diagnosis" is awarded Best Article for Vol 13 issue 01
A Study by Badritdinova MN et al. entitled "Peculiarities of a Pain in Patients with Ischemic Heart Disease in the Presence of Individual Combines of the Metabolic Syndrome" is awarded Best Article for Vol 12 issue 24
A Study by Sindhu Priya E S et al. entitled "Neuroprotective activity of Pyrazolone Derivatives Against Paraquat-induced Oxidative Stress and Locomotor Impairment in Drosophila melanogaster" is awarded Best Article for Vol 12 issue 23
A Study by Habiba Suhail et al. entitled "Effect of Majoon Murmakki in Dysmenorrhoea (Usre Tams): A Standard Controlled Clinical Study" is awarded Best Article for Vol 12 issue 22
A Study by Ghaffar UB et al. entitled "Correlation between Height and Foot Length in Saudi Population in Majmaah, Saudi Arabia" is awarded Best Article for Vol 12 issue 21
A Study by Siti Sarah Binti Maidin entitled "Sleep Well: Mobile Application to Address Sleeping Problems" is awarded Best Article for Vol 12 issue 20
A Study by Avijit Singh"Comparison of Post Operative Clinical Outcomes Between “Made in India” TTK Chitra Mechanical Heart Valve Versus St Jude Mechanical Heart Valve in Valve Replacement Surgery" is awarded Best Article for Vol 12 issue 19
A Study by Sonali Banerjee and Mary Mathews N. entitled "Exploring Quality of Life and Perceived Experiences Among Couples Undergoing Fertility Treatment in Western India: A Mixed Methodology" is awarded Best Article for Vol 12 issue 18
A Study by Jabbar Desai et al. entitled "Prevalence of Obstructive Airway Disease in Patients with Ischemic Heart Disease and Hypertension" is awarded Best Article for Vol 12 issue 17
A Study by Juna Byun et al. entitled "Study on Difference in Coronavirus-19 Related Anxiety between Face-to-face and Non-face-to-face Classes among University Students in South Korea" is awarded Best Article for Vol 12 issue 16
A Study by Sudha Ramachandra & Vinay Chavan entitled "Enhanced-Hybrid-Age Layered Population Structure (E-Hybrid-ALPS): A Genetic Algorithm with Adaptive Crossover for Molecular Docking Studies of Drug Discovery Process" is awarded Best article for Vol 12 issue 15
A Study by Varsha M. Shindhe et al. entitled "A Study on Effect of Smokeless Tobacco on Pulmonary Function Tests in Class IV Workers of USM-KLE (Universiti Sains Malaysia-Karnataka Lingayat Education Society) International Medical Programme, Belagavi" is awarded Best article of Vol 12 issue 14, July 2020
A study by Amruta Choudhary et al. entitled "Family Planning Knowledge, Attitude and Practice Among Women of Reproductive Age from Rural Area of Central India" is awarded Best Article for special issue "Modern Therapeutics Applications"
A study by Raunak Das entitled "Study of Cardiovascular Dysfunctions in Interstitial Lung Diseas epatients by Correlating the Levels of Serum NT PRO BNP and Microalbuminuria (Biomarkers of Cardiovascular Dysfunction) with Echocardiographic, Bronchoscopic and HighResolution Computed Tomography Findings of These ILD Patients" is awarded Best Article of Vol 12 issue 13 
A Study by Kannamani Ramasamy et al. entitled "COVID-19 Situation at Chennai City – Forecasting for the Better Pandemic Management" is awarded best article for  Vol 12 issue 12
A Study by Muhammet Lutfi SELCUK and Fatma entitled "Distinction of Gray and White Matter for Some Histological Staining Methods in New Zealand Rabbit's Brain" is awarded best article for  Vol 12 issue 11
A Study by Anamul Haq et al. entitled "Etiology of Abnormal Uterine Bleeding in Adolescents – Emphasis Upon Polycystic Ovarian Syndrome" is awarded best article for  Vol 12 issue 10
A Study by entitled "Estimation of Reference Interval of Serum Progesterone During Three Trimesters of Normal Pregnancy in a Tertiary Care Hospital of Kolkata" is awarded best article for  Vol 12 issue 09
A Study by Ilona Gracie De Souza & Pavan Kumar G. entitled "Effect of Releasing Myofascial Chain in Patients with Patellofemoral Pain Syndrome - A Randomized Clinical Trial" is awarded best article for  Vol 12 issue 08
A Study by Virendra Atam et. al. entitled "Clinical Profile and Short - Term Mortality Predictors in Acute Stroke with Emphasis on Stress Hyperglycemia and THRIVE Score : An Observational Study" is awarded best article for  Vol 12 issue 07
A Study by K. Krupashree et. al. entitled "Protective Effects of Picrorhizakurroa Against Fumonisin B1 Induced Hepatotoxicity in Mice" is awarded best article for issue Vol 10 issue 20
A study by Mithun K.P. et al "Larvicidal Activity of Crude Solanum Nigrum Leaf and Berries Extract Against Dengue Vector-Aedesaegypti" is awarded Best Article for Vol 10 issue 14 of IJCRR
A study by Asha Menon "Women in Child Care and Early Education: Truly Nontraditional Work" is awarded Best Article for Vol 10 issue 13
A study by Deep J. M. "Prevalence of Molar-Incisor Hypomineralization in 7-13 Years Old Children of Biratnagar, Nepal: A Cross Sectional Study" is awarded Best Article for Vol 10 issue 11 of IJCRR
A review by Chitra et al to analyse relation between Obesity and Type 2 diabetes is awarded 'Best Article' for Vol 10 issue 10 by IJCRR. 
A study by Karanpreet et al "Pregnancy Induced Hypertension: A Study on Its Multisystem Involvement" is given Best Paper Award for Vol 10 issue 09

List of Awardees

A Study by Ese Anibor et al. "Evaluation of Temporomandibular Joint Disorders Among Delta State University Students in Abraka, Nigeria" from Vol 13 issue 16 received Emerging Researcher Award

A Study by Alkhansa Mahmoud et al. entitled "mRNA Expression of Somatostatin Receptors (1-5) in MCF7 and MDA-MB231 Breast Cancer Cells" from Vol 13 issue 06 received Emerging Researcher Award

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Disclaimer: International Journal of Current Research and Review (IJCRR) provides platform for researchers to publish and discuss their original research and review work. IJCRR can not be held responsible for views, opinions and written statements of researchers published in this journal.


International Journal of Current Research and Review (IJCRR) provides platform for researchers to publish and discuss their original research and review work. IJCRR can not be held responsible for views, opinions and written statements of researchers published in this journal


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