A Novel Hybrid Deep Learning Model for Detecting Breast Cancer

Cancer is a deformity of the body cells that grow out of control and spread to other parts of body. According to the American Cancer Society, early identification of cancer resulted in a 99% chance of survival in the localized stage. In this article, a benchmark cancer dataset is considered for the purpose of investigation. After the preprocessing of data, we attempt to predict cancer type using our proposed deep learning model named as CNN-Soft-T that combines the feature selection (FS) and classification. FS is introduced using histogram technique for  anomaly detection and dimension reduction. We have compared the performance of this model to other cutting-edge machine learning approaches including Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN) and Decision Tree (DT) in order to demonstrate its utility. We have experimentally exhibited the exceptional empirical effectiveness and biological applicability of our suggested hybrid model for cancer diagnostics and medication development.