Medical image Classification using Transfer Learning: Convolutional Neural Network Models approach

Abstract

A relatively low life expectancy in their greatest grade is caused by more debilitating diseases in the medical sector. Any kind of misdiagnosis may lead to improper medical intervention and lower the likelihood that a patient will survive. Making an appropriate treatment plan to cure and enhance the quality of life for patients suffering from any kind of illness starts with an accurate diagnosis. Convolutional neural networks and computer-aided disease detection systems have produced success stories and advanced the science of machine learning significantly. Compared to more conventional neural network layers, the deep convolutional layers automatically extract significant and reliable characteristics from the input space. Within the suggested structure, we use two convolutional neural network architectures (VGG16 and VGG19) to perform two types of medical imaging (x-ray and MRI) investigations in order to categorize brain tumors as normal and up normal as well as classify x-ray pneumonia or normal. The transfer learning strategies by VGG16 and vgg19 models that is, using MRI and x-ray to fine-tune and freeze are then examined in each investigation. In order to increase dataset samples and decrease the likelihood of over-fitting, data augmentation techniques are performed to MRI slices and x-rays for results that can be more broadly utilized. In the proposed studies, the fine-tune VGG19, VGG16 architecture attained highest accuracy up to 0.95with x-ray and 0.80 respectively. The accuracy0.98 used MRI in terms of classification and detection with VGG19.