Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning

• URL: http://arxiv.org/abs/2206.01088v2
• Date: Fri, 3 Jun 2022 05:40:38 GMT
• Title: Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning
• Authors: Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin, Arnisha Akhter, Khondokar Fida Hasan, Mohammad Ali Moni
• Abstract summary: We introduce a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. Our model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively.
• Score: 0.9786690381850355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.

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