Deep Learni̇ng Archi̇techture for Classi̇fi̇cati̇on of Breast Cancer Cells i̇n Fluorescence Mi̇croscopy Images

Abstract

Biological cell classification plays a significant role in the field of biomedical research. Cell classification is useful in different biomedical applications like identification of a normal and abnormal cell, cancer cell recognition, behovioural studies of cells to different drugs, etc. Automated cell classification techniquies would assist the radiologist for the disease diagnoses and to grasp the severity of the disease based on the intricate intracellular structures of the cells. In this work, a deep learning method based on EfficientNet is designed for automatic classification of human breast cancer cells from fluorescence microscopy images. More specifically, transfer learning is employed to take the advantage of the pretrained model and further improvising the performance of the network by fine tuning several of last layers for learning the specific classification task. The proposed deep learning method is evaluated on human breast cancer cells, which gave 98.15% accuracy, 98.33% precision, 98.15% recall and 98.14% F1 score. Comparitive analysis of the proposed method with the standard architectures is also performed to assert the efficacy of our model.