OHMBC: Optimized Hybrid deep learning Model for Classification of Breast Cancer
Binju Saju, V Asha, Neethu Tressa, Pooja B Kondikoppa, Pooja Pooja, Pavan Kumar V J · 2024
Men are much less likely than women to develop breast cancer. Breast lumps, bloody nipple discharge, and changes in the nipple's or breast's shape or texture are all indications of breast cancer. The study proposes a hybrid model to classify Diabetic retinopathy images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained 96% accuracy, 98.1% sensitivity, 98.2% specificity, 98% precision, and 98.18% F1 Score.