mlsanityData to 14 Oct 2026

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

arXiv 2206.01088 ↗First posted 2 Jun 2022eess.IV cs.CV cs.LGcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 8 of its 93 references that are in the map. First posted on arXiv 2 Jun 2022.

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8 of 93 references.

References · 8

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  4. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  5. 2019Lung and Colon Cancer Histopathological Image Dataset (LC25000)cited by 89
  6. 2021Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency mapscited by 7
  7. 2019Double Transfer Learning for Breast Cancer Histopathologic Image Classificationcited by 6
  8. 2020Convolution Neural Networks for diagnosing colon and lung cancer histopathological imagescited by 5

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Citing papers per year: 2022: 1; 2023: 3; 2024: 9.

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  7. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  8. 2024Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinomacited by 1
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  10. 2023Machine Learning Approach for Cancer Entities Association and Classificationcited by 0
  11. 2024Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scanscited by 0
  12. 2024An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Techniquecited by 0
1 more
  1. 2024Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology imagescited by 0