Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images

Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classification of cancer types are crucial for effective treatment. Imaging tests on different image modalities such as Histopathology images, provide valuable insights into the cellular and architectural features of tissues, allowing pathologists to make diagnosis, determine disease stages, and guide treatment decisions. They are an essential tool in the study and understanding of diseases, aiding in research, education, and patient care. Convolutional neural network based pretrained deep learning models can be used successfully to detect lung cancer. In this study, we have used a channel attention-enabled deep learning model as a feature extractor followed by an adaptive Genetic Algorithm (GA) based feature selector. Here, we calculate the fitness score of each chromosome (i.e., a candidate solution) using a filter method, instead of a classifier. Further, the GA optimized feature vector is fed to the K-nearest neighbors classifier for final classification. The proposed method shows a promising result with an overall accuracy of 99.75% on the LC25000 dataset, which is a publicly available dataset of lung histopathological images. The source code for this work can be found https://github.com/priyam-03/GA-Feature-Selector-Lung-Cancer .