⚡ Quick Summary
This study introduces an adaptive genetic algorithm based deep feature selector for the detection of lung cancer in histopathological images, achieving an impressive overall accuracy of 99.75% on the LC25000 dataset. The integration of a channel attention-enabled deep learning model with a K-nearest neighbors classifier demonstrates a significant advancement in cancer detection methodologies. 🎉
🔍 Key Details
- 📊 Dataset: LC25000, a publicly available dataset of lung histopathological images
- 🧩 Features used: Channel attention-enabled deep learning model as a feature extractor
- ⚙️ Technology: Adaptive Genetic Algorithm (GA) for feature selection
- 🏆 Performance: Overall accuracy of 99.75%
🔑 Key Takeaways
- 🦠 Cancer detection is crucial for effective treatment and management.
- 💡 Early detection can significantly improve patient outcomes.
- 🤖 Deep learning models can enhance the accuracy of cancer classification.
- 🏅 The proposed method utilizes an adaptive GA for optimized feature selection.
- 📈 The study achieved a remarkable accuracy of 99.75% on the LC25000 dataset.
- 🔗 Source code is available for further research and application.
- 🌍 This research contributes to the ongoing efforts in cancer detection technology.
📚 Background
Cancer remains a significant global health challenge, with high mortality rates and various affected organs. The ability to detect cancer early and classify its types accurately is essential for effective treatment strategies. Imaging techniques, particularly histopathology, provide critical insights into tissue characteristics, aiding pathologists in diagnosis and treatment planning.
🗒️ Study
The study employed a channel attention-enabled deep learning model to extract features from lung histopathological images. Following this, an adaptive Genetic Algorithm (GA) was utilized to select the most relevant features, optimizing the feature vector for classification. The final classification was performed using a K-nearest neighbors classifier, showcasing the effectiveness of this combined approach.
📈 Results
The proposed method demonstrated a remarkable overall accuracy of 99.75% on the LC25000 dataset. This high level of accuracy indicates the potential of using advanced deep learning techniques and genetic algorithms in the field of cancer detection, paving the way for more reliable diagnostic tools.
🌍 Impact and Implications
The findings of this study could have profound implications for cancer detection technologies. By leveraging deep learning and genetic algorithms, healthcare professionals can enhance diagnostic accuracy, leading to earlier interventions and improved patient outcomes. This research underscores the importance of integrating innovative technologies in medical diagnostics, potentially transforming cancer care practices.
🔮 Conclusion
This study highlights the significant potential of combining deep learning with genetic algorithms for cancer detection in histopathological images. The impressive accuracy achieved suggests that such methodologies could revolutionize the way we approach cancer diagnostics. Continued research in this area is essential for further advancements and applications in clinical settings.
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Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images.
Abstract
Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classification of cancer types are crucial for effective treatment. Imaging tests on different image modalities such as Histopathology images, provide valuable insights into the cellular and architectural features of tissues, allowing pathologists to make diagnosis, determine disease stages, and guide treatment decisions. They are an essential tool in the study and understanding of diseases, aiding in research, education, and patient care. Convolutional neural network based pretrained deep learning models can be used successfully to detect lung cancer. In this study, we have used a channel attention-enabled deep learning model as a feature extractor followed by an adaptive Genetic Algorithm (GA) based feature selector. Here, we calculate the fitness score of each chromosome (i.e., a candidate solution) using a filter method, instead of a classifier. Further, the GA optimized feature vector is fed to the K-nearest neighbors classifier for final classification. The proposed method shows a promising result with an overall accuracy of 99.75% on the LC25000 dataset, which is a publicly available dataset of lung histopathological images. The source code for this work can be found https://github.com/priyam-03/GA-Feature-Selector-Lung-Cancer .
Author: [‘Roy A’, ‘Saha P’, ‘Gautam N’, ‘Schwenker F’, ‘Sarkar R’]
Journal: Sci Rep
Citation: Roy A, et al. Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images. Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images. 2025; 15:4803. doi: 10.1038/s41598-025-86362-8