Breast Health Examination Using Benign/Malignant Breast Histology Slides with Lightweight and Conventional Deep Learning Scheme

Abstract

Breast cancer (BC) is one of the harsh cancers which lead to severe health issues worldwide. Premature detection and treatment implementation is important for treating the BC with a suitable clinical handing process. Proposed research implements a deep-learning (DL) tool for identifying benign/malignant BC from the histopathology data. This work considered benign/malignant class image data for examination and outcome is verified to substantiate the developed system's merit. Various phases found in this scheme includes; (i) image augmentation, colour normalization and resizing, (ii) features extraction and image classification with softmax, (iii) best features identification and fused-features generation, (iv) performance confirmation with fused-features along with 3-fold cross validation. Proposed work implemented conventional and lightweight DL-models for verifying its performance and the experimental outcome confirms that the developed system provides up to >99% accuracy. The performance of lightweight and conventional models is separately evaluated and this research confirms that the conventional scheme offers a better result.