MEDICAL IMAGE CLASSIFICATION USING DEEP LEARNING TECHNIQUE

Abstract

The research in medical image classification using Deep Learning Techniques is a major trend these days as it serves best in medical diagnosis. Deep Learning Models helps to recognize the patterns with promising accuracy that task may be challenging for medical practitioners due to time constraints and performing comparison with large datasets. As we are testing the maximum limit of technology and going exponentially in the field. Similarly this study was intended to reduce human effort by utilizing Deep Learning technologies in the field of medical by classifying Gastrointestinal (GI) images from an open source Dataset KVASIR V2 with all 8 classes. In most of the previous studies dataset was found as the main limitation as limited number of samples and classes were used. As compare to previous research this thesis, does image classification on increased number of classes with promising accuracy and identifies the suitable parameters. Initial results were carried of 3 CNN based models out of which two models EfficientNetV2B2 and VGG-16 were pretrained on ImageNet Dataset while the third model was AlexNet. Evaluation metrics reported in this study include accuracy, precision, recall, f1- measure in addition to categorical accuracies and Confusion matrix. Based on above metrics we selected the best model EfficientNetV2B2 for further fine tuning. The study achieved promising results in classification of GI Images based on pretrained model Efficient Net V2B2 with SGD optimizer by achieving training accuracy of 97.03%, validation accuracy 94.03%, while testing accuracy of 95.34%. Transfer learning technique was tested by utilizing above two pretrained models as a foundation, transfer learning proved to be great for substantial reductions in training time and processing resources beside AlexNet. Further utilizing Transfer learning leaded to better training and improved performance in classification of Gastrointestinal (GI) medical images. Lastly an application with GUI interface was built using Tkinter python library to better interact with image classification process.