Local Binary Pattern (LBP) and Transfer Learning Based Approach to Classify Lung and Colon Cancer

Onkar Singh, Koushlendra Kumar Singh

Abstract: Several genetic disorders and other metabolic abnormalities work together to generate the lethal disease known as cancer. Today’s most contributing factors to mortality and disability in patients are lung and colon cancer. A World Health Organization (WHO) 2020 report listed cancer as one of the leading causes of mortality globally. About 2.735 million of these fatalities were caused by lung and colon cancer combined. A critical component of the patient’s treatment is the diagnosis of lung cancer by histopathology. Therefore, one of the leading research priorities, mostly in the domain of biomedical health information systems, is the identification and categorization of lung and colon cancer. The present article encompasses the Local Binary Pattern (LBP) and transfer learning-based approaches to classify lung as well as colon cancer. LBP has been used for extracting features, and transfer learning has been used for the classification of lung and colon cancer. Histopathological (LC25000) lung and colon datasets are used to validate the proposed methodology. The results of the proposed method have also been compared with different existing methods and reported in the article. Our proposed method has an average accuracy of 99.00% and a F1 score of 99.2%, whereas precision and recall have 99.4% for lung and colon cancer detection. The results of the investigation demonstrate that our suggested technique greatly outperforms current models.

External IDs:dblp:journals/sncs/SinghS24