Lung Cancer detection using Deep Learning (2501.07197v1)

Abstract: In this paper we discuss lung cancer detection using hybrid model of Convolutional-Neural-Networks (CNNs) and Support-Vector-Machines-(SVMs) in order to gain early detection of tumors, benign or malignant. The work uses this hybrid model by training upon the Computed Tomography scans (CT scans) as dataset. Using deep learning for detecting lung cancer early is a cutting-edge method.

• The paper demonstrates a CNN-SVM hybrid approach that significantly improves lung CT scan classification for early cancer detection.
• It employs advanced data augmentation techniques to convert raw CT images into robust features despite imbalanced datasets.
• The model outperforms traditional methods by reducing overfitting and achieving precision rates of 80-90% and recall rates of 85-95%.

Lung Cancer Detection Using CNN-SVM Hybrid Model

The paper presents a sophisticated approach to early lung cancer detection utilizing a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). This research addresses the challenge of accurate classification of lung CT scans to distinguish between benign and malignant nodules, leveraging the complementary strengths of CNNs in feature extraction and SVMs in optimal decision boundary formation.

Data Augmentation and Model Training

To enhance the robustness of the model against the typically sparse and imbalanced datasets in medical imaging, data augmentation techniques are employed. These include rotation, flipping, and brightness adjustment, implemented using Python libraries like skimage and scikit-learn, thereby expanding the dataset for improved training outcomes. During training, CNNs are tasked with extracting nuanced features from CT images, identifying textures, shapes, and patterns indicative of malignancies. The CNN operates hierarchically to isolate minor anomalies. Post feature extraction, SVMs classify the images, deftly navigating the decision boundaries to discern healthy from malignant tissue.

Comparative Analysis with Other Models

The paper contrasts the proposed CNN-SVM model with traditional ML models like Random Forests and isolated CNN architectures. While traditional models falter with unstructured data, CNNs excel in feature extraction despite overfitting risks in small datasets due to reliance on the softmax classification. Conversely, the CNN-SVM hybrid sidesteps such drawbacks by coupling CNNs' extraction capabilities with the robust classification strength of SVMs, particularly useful in binary classification of benign versus malignant nodules. Compared to standalone CNNs, the hybrid model dynamically adjusts decision boundaries, reducing overfitting and maximizing class separation, thereby achieving superior generalization capabilities even with imbalanced datasets.

Methodological Framework

CT scan datasets, particularly from Kaggle, serve as the training ground for this hybrid model. Preprocessing steps include normalization to convert Hounsfield Units into ranges suitable for deep learning and lung segmentation to remove irrelevant areas. Noise reduction through Gaussian filtering enhances image quality, crucial for deep learning accuracy. The CNNs efficiently conduct feature extraction via convolutional and pooling layers, transforming raw images into feature-rich vectors fed into the SVM classifier. By implementing the kernel trick, the SVM classifier adeptly handles non-linear feature spaces, optimizing the decision boundary for improved classification efforts.

Performance Metrics

The efficacy of the hybrid model is underscored by impressive performance metrics, including a precision range between 80% to 90% and a recall rate from 85% to 95%. The F1-Score oscillates around 85% to 92%, indicative of a well-balanced trade-off between prediction precision and recall, thereby minimizing the risks of false negatives, critical in medical diagnostics.

Conclusion and Implications

The CNN-SVM hybrid model exemplifies a promising advancement in lung cancer detection frameworks, lending itself well to potential clinical applications due to its heightened precision and diagnostic accuracy. The model proposes an effective balance between computational efficiency and robustness, offering a viable pathway toward early lung cancer detection. Future explorations might include extending the current framework with ensemble models, real-world clinical validations, and continuous learning mechanisms. This research augments the discourse on AI utilization in medical diagnostics, furnishing a reliable model for consequential healthcare improvements.