Automated Routine Colon Cancer Nuclei Classification Using Black Widow Optimization with Deep Learning Model

Precise and efficient classification of histological cell nuclei is of great significance because of its promising application in the domain of medical image analysis. It facilitates the physician to explore various factors and better understand the treatment of cancer. Due to cellular heterogeneity detection and classification of cell nuclei in histopathology images of tissue stained with the typical haematoxylin and eosin stain becomes a tedious process. The deep learning approach has been demonstrated to produce remarkable outcomes on histopathology images in different fields. Therefore, this study designs an Automated Routine Colon Cancer Nuclei Classification Using Black Widow Optimization with Deep Learning (ARCCNC-BWODL) model. The presented ARCCNC-BWODL algorithm focuses majorly on the identification and classification of RCC cell nuclei. The presented algorithm applies improved Faster SqueezeNet model to make feature vectors. Besides, the hyperparameter tuning of the Faster SqueezeNet approach is performed via the BWO system. To classify the nuclei effectively, long short term memory is used. The simulation outcome of the ARCCNC-BWODL model was tested on medical imaging database and the outcomes exhibited the enhancements of the ARCCNC-BWODL over other DL techniques.