Özet:

Abstract In this study, we apply deep learning techniques to create a complete CAD system for accurate and efficient IDC/non-IDC categorization. This CAD system has two distinct classification methods: machine learning-based classification using a variety of classifiers and deep learning-based classification using a specially constructed convolutional-neural network (CNN). Kaggle, a publicly accessible benchmark database, is used to accomplish this study. Accuracy, sensitivity, specificity, false positive rate, classification error, and precision are only few of the performance metrics used to assess machine learning and deep learning classifiers. Accuracy and sensitivity are selected as the primary characteristics by which the best classifier is evaluated. The purpose of this new saliency detection method is to aid in the diagnosis of invasive ductal carcinoma (IDC) by using IDC histopathology pictures to train deep learning algorithms.

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