Article
CANCER CLASSIFICATION UTILIZING MEDICAL IMAGES
Medical imaging plays a vital role in the early detection and treatment monitoring of lung cancer. Common imaging techniques such as chest X-rays, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and other molecular imaging methods have long been used to diagnose lung cancer. Despite their widespread use, these modalities face certain limitations particularly their lack of automated capabilities for accurately classifying cancerous tissues. This challenge becomes even more critical in patients with overlapping or co-existing conditions, where misdiagnosis can delay treatment. To address this gap, there is a growing demand for diagnostic tools that are not only highly sensitive but also precise in identifying early-stage lung cancer. In recent years, deep learning has emerged as a transformative technology in medical imaging, offering robust capabilities for analyzing both visual and textural data. Leveraging this potential, the present study introduces an enhanced convolutional neural network (CNN) model specifically designed for detecting and classifying lung cancer from chest CT scans. The proposed CNN architecture is capable of categorizing images into four distinct classes: adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal tissue. When benchmarked against traditional machine learning approaches specifically the Naïve Bayes Classifier (NBC) the deep CNN model demonstrated superior performance and higher classification accuracy. Evaluation metrics further underscore the model’s effectiveness, suggesting that it can significantly aid clinicians in making more accurate and timely diagnoses, ultimately improving patient outcomes
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