2019/08/20 by Chandra Churh Chatterjee, Chatterjee, Chandra Churh, Gopal Krishna +1 · 1 citation
Computer Science · Medicine · Neuroscience · #AI in cancer detection #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1908.07362
openalex publication_date 2019/08/20 · openalex created_date 2022/07/19 · openalex updated_date 2026/07/28
Invasive ductal carcinoma (IDC), which is also sometimes known as the\ninfiltrating ductal carcinoma, is the most regular form of breast cancer. It\naccounts for about 80% of all breast cancers. According to the American Cancer\nSociety, more than 180,000 women in the United States are diagnosed with\ninvasive breast cancer each year. The survival rate associated with this form\nof cancer is about 77% to 93% depending on the stage at which they are being\ndiagnosed. The invasiveness and the frequency of the occurrence of these\ndisease makes it one of the difficult cancers to be diagnosed. Our proposed\nmethodology involves diagnosing the invasive ductal carcinoma with a deep\nresidual convolution network to classify the IDC affected histopathological\nimages from the normal images. The dataset for the purpose used is a benchmark\ndataset known as the Breast Histopathology Images. The microscopic RGB images\nare converted into a seven channel image matrix, which is then fed to the\nnetwork. The proposed model produces a 99.29% accurate approach towards the\nprediction of IDC in the histopathology images with an AUROC score of 0.9996.\nClassification ability of the model is tested using standard performance\nmetrics.\n