A novel variant of deep convolutional neural network for classification of ovarian tumors using CT images
Ashwini Kodipalli, Susheela V Devi, Santosh Dasar, Taha Ismail
Abstract
Deep Learning models have shown tremendously impressive performance on image classification tasks. In the medical imaging domain, progress has been made in obtaining high-quality data for analysis and using state-of-the- art artificial intelligence algorithms for solving complex problems and providing answers to key questions using data. One such problem that is of crucial importance and interest to medical researchers is to classify tumors into two categories benign and malignant. This research work focuses on proposing a novel variation of CNN architecture and a comparison of the performances of state-of-the-art ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winning architectures for the task of classifying ovarian tumors by training and evaluating images on a dataset of ovarian CT scan images with the help of cloud services such as Google Cloud Platform. The proposed architecture has attained an accuracy of 97.53% and outperformed the existing CNN variants.