Deep Learning Architechture for classification of Breast cancer Cells in Fluorescence Microscopy 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 architechutre 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 architechture is evaluated on human breast cancer cells, which gave 98.15% accuracy, precision, recall and F1 score. Comparitive analysis of the proposed architechture with the standard architechures is also performed to assert the efficacy of our model.