Author(s) :

Rama Krishna Raju Chekuri.

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Abstract :

Abstract Lung cancer, one of the most fatal diseases worldwide, necessitates early and accurate diagnosis to improve survival rates. This study explores the application of Convolutional Neural Networks (CNNs) for automating the detection of lung cancer, specifically leveraging histopathological biopsy images. To address the limitations of manual diagnosis, including its time-consuming and error-prone nature, a pre-trained EfficientNet-based model was employed for precise image classification. The proposed approach classifies lung cancer into benign and malignant categories, demonstrating remarkable performance with robust accuracy and interpretability. The model was fine-tuned to enhance subtype differentiation and adapt to real-time intraoperative analysis, showcasing its scalability to other cancer types as well. Experimental results reveal that the EfficientNet architecture not only surpasses conventional CNN models in terms of accuracy and efficiency but also minimizes computational requirements, making it a viable tool for large-scale clinical implementation. This project underscores the transformative potential of machine learning in radiology and oncology, paving the way for improved diagnostic precision, personalized treatments, and better patient outcomes.

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