Machine Learning Based Multi-Class Classification and Grading of Squamous Cell Carcinoma in Optical Microscopy.

ABSTRACT: Histopathological tissue grading is critical for disease diagnosis and treatment, but manual grading is labor-intensive and time-consuming, requiring expert pathologists. This study presents an efficient analysis of squamous cell carcinoma (SCC) histopathological images using machine learning (ML) and deep learning (DL) models. Five ML models-support vector machine, Naïve Bayes, decision tree, k-nearest neighbor (KNN), and neural network-were trained with 5-, 7-, and 10-fold cross-validation. Discrete wavelet transform along with gray level co-occurrence matrix and histogram features extracted 360 features per image, and Student's t-test selected 114 key features. Among ML models, KNN with sevenfold cross-validation achieved 98% accuracy. Additionally, a convolutional neural network (CNN)

SUBMITTER: Kaniyala Melanthota S 

PROVIDER: S-EPMC12521958 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature