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

arXiv: https://arxiv.org/abs/2206.01088 · HTML: https://mlsanity.com/paper/2206.01088/

## 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.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Lung and Colon Cancer Histopathological Image Dataset (LC25000)](https://mlsanity.com/paper/1912.12142/) (2019)
- [Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps](https://mlsanity.com/paper/2103.12155/) (2021)
- [Double Transfer Learning for Breast Cancer Histopathologic Image Classification](https://mlsanity.com/paper/1904.07834/) (2019)
- [Convolution Neural Networks for diagnosing colon and lung cancer histopathological images](https://mlsanity.com/paper/2009.03878/) (2020)

## Built on by (most cited first, 13 of 13)

- [Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction](https://mlsanity.com/paper/2401.12262/) (2024)
- [A Dependable Hybrid Machine Learning Model for Network Intrusion Detection](https://mlsanity.com/paper/2212.04546/) (2022)
- [Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture](https://mlsanity.com/paper/2311.16593/) (2023)
- [MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs](https://mlsanity.com/paper/2402.13277/) (2024)
- [An Optimized Ensemble Deep Learning Model For Brain Tumor Classification](https://mlsanity.com/paper/2305.12844/) (2023)
- [Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search](https://mlsanity.com/paper/2402.14389/) (2024)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma](https://mlsanity.com/paper/2402.17807/) (2024)
- [A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats](https://mlsanity.com/paper/2403.13010/) (2024)
- [Machine Learning Approach for Cancer Entities Association and Classification](https://mlsanity.com/paper/2306.00013/) (2023)
- [Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans](https://mlsanity.com/paper/2403.05353/) (2024)
- [An Integrated Deep Learning Model for Skin Cancer Detection Using Hybrid Feature Fusion Technique](https://mlsanity.com/paper/2410.14489/) (2024)
- [Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images](https://mlsanity.com/paper/2412.03084/) (2024)

## 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.