Lung Cancer Histopathological Image Classification Using ‎Custom CNN Model

  • Authors

    • Asiya Research Scholar, Department of CSE, Noorul Islam Centre for Higher Education, Tamil Nadu, India
    • Dr. N. Sugitha Department of ECE, Saveetha Engineering College, Thandalam, Chennai, Tamil Nadu, India‎
    https://doi.org/10.14419/hvw82e84

    Received date: May 28, 2025

    Accepted date: May 31, 2025

    Published date: July 8, 2025

  • Lung Cancer Diagnosis; Histopathology Images; Deep Learning; Neural Networks

  • Abstract

    Lung cancer accounts for the highest death rate and disability among harmful tumors. For decades, researchers have focused on screening ‎for lung cancer in an attempt to reduce the high associated mortality rate. Treatment options also have greatly improved in the last few years. ‎An equally important step performed by pathologists is stang, typing and subtyping cancers which involves visual review of histopathology ‎slides. Classifying the two most prevalent types of lung cancer, carcinomas of the squamous cell and adenocarcinoma types, needs a skilled ‎pathologist to physically examine a material. Here, we provide Custom Developed 2D-CNN, a model for a neural network that can classify ‎images of lung histology. Lung as well as colon cancer LC25000 histopathology picture collection and, upon which our research was based, ‎are detailed in conjunction with the model. This dataset contains 5000 digital histopathology images labelled as benign (normal cell), ‎adenocarcinoma (cancer cell) and squamous carcinoma (cancer cell). Various feature extraction techniques were employed to preprocess the ‎images before feeding them to the neural network model. For data sorting, the VGG16 model as well as a customized 2D-CNN model were ‎used. The Custom Developed 2D-CNN attained 95% accuracy. The outcomes generated by the advanced classification approach DL (deep ‎learning)-based, show that it can distinguish between different kinds of lung cancer. Using fewer attributes and lower computational ‎complexity than conventional approaches. Additionally, these results demonstrate that certain transformation methods increase data ‎explanatory power as well as simplify and enhance the diagnostic process‎.

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    Asiya, & Sugitha , D. N. . (2025). Lung Cancer Histopathological Image Classification Using ‎Custom CNN Model. International Journal of Basic and Applied Sciences, 14(SI-1), 279-287. https://doi.org/10.14419/hvw82e84