DESIGN AND DEVELOPMENT OF SAILFISH OPTIMIZER AND TANGENT SEARCH ALGORITHM FOR LUNG AND COLON CANCER CLASSIFICATION WITH COMPARATIVE ANALYSIS WITH CNN-VGG 16, DHS NET, MA NET
DOI:
https://doi.org/10.34218/IJCET_16_02_005Keywords:
Histopathological Image, Lung Cancer, Colon Cancer, Lung And Colon Cancer Detection, Sailfish Optimizer (SO), Tangent Search Algorithm, CatBoost Algorithm, Histopathological Diagnosis, Machine LearningAbstract
Lung cancer is a harmful growth that starts in the lungs and colon cancer begins in the rectum and develops into malignant tumors, with adenocarcinoma being the most prevalent subtype. The diagnosis and treatment of LCC are dependent on histopathological techniques, which are often time-consuming and require skilled pathologists. Analysis of histopathological images (HIs) has become a powerful diagnostic technique for cancer. The lung and colon cancers are the most prevalent and deadly of the several forms of cancer. To diagnose lung and colon cancer (LCC), HI analysis entails analyzing and examining tissue samples taken from the LCC to identify tumors or malignant cells.The texture, morphology, and color are the most important categories of HPI features, and each category contains multiple sub-features. The feature selection process plays an important role in machine learning-based image detection, as it helps to improve model performance and interpretability while avoiding overfitting and computational costs. However, the absence of an effective feature selection model is a concern. At the same time, a single feature selection algorithm is inadequate to select the appropriate features because each has its own merits. Additionally, it has significant limitations that can affect the quality of the feature selection process. To address these challenges, we presented hybrid feature selection methods such as Sailfish Optimizer (SO) and tangent search algorithm (TSA), one that focuses on selecting accurate and correct features while another focuses on speeding up the process. The proposed strategy has a fast convergence rate when compared to existing feature selection techniques. In the classification step on the LC25000 dataset, a CatBoost classifier makes the ultimate determination. According to the simulation findings, the proposed approach can categorize data with a maximum accuracy of 0.9852%.
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