Version 1
: Received: 30 August 2023 / Approved: 4 September 2023 / Online: 5 September 2023 (03:23:02 CEST)
How to cite:
Shanmugam, K.; Rajaguru, H. Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images. Preprints2023, 2023090160. https://doi.org/10.20944/preprints202309.0160.v1
Shanmugam, K.; Rajaguru, H. Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images. Preprints 2023, 2023090160. https://doi.org/10.20944/preprints202309.0160.v1
Shanmugam, K.; Rajaguru, H. Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images. Preprints2023, 2023090160. https://doi.org/10.20944/preprints202309.0160.v1
APA Style
Shanmugam, K., & Rajaguru, H. (2023). Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images. Preprints. https://doi.org/10.20944/preprints202309.0160.v1
Chicago/Turabian Style
Shanmugam, K. and Harikumar Rajaguru. 2023 "Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images" Preprints. https://doi.org/10.20944/preprints202309.0160.v1
Abstract
Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. One such method uses computer aided detection to classify as benign or malignant from the histopathological images. Standard histopathological images were used from a Lung and Colon Cancer Histopathological Image Dataset (LC25000) which contains two classes of benign and malignant of 5000 each. Images were preprocessed and features extracted using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Feature selection methods used are KL Divergence and Invasive Weed Optimization (IWO). Seven different classifiers like SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without Feature selection and Hyperparameter Tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
