Abstract: This study explores the application of convolutional neural networks (CNNs) in biomedical image analysis for the detection of colon and lung cancer. Leveraging the power of deep learning, we aim to develop a robust and accurate system capable of identifying cancerous lesions in colonoscopy and lung CT scan images.The research begins with the collection and preprocessing of a sizable dataset containing annotated medical images. Various data augmentation and normalization techniques are applied to enhance dataset diversity and model generalization. Subsequently, a CNN architecture is carefully designed, either adapting existing architectures or crafting custom ones tailored to the unique characteristics of medical images.
Training the CNN involves splitting the dataset into training, validation, and testing sets, and employing optimization algorithms to minimize a chosen loss function. Hyperparameter tuning and validation set monitoring ensure the prevention of overfitting and the optimization of model performance.Evaluation of the trained model includes rigorous testing on held-out data to assess its accuracy, precision, recall, F1-score, and AUC-ROC. Error analysis aids in understanding the model's weaknesses and identifying avenues for improvement.Ultimately, the developed model holds promise for deployment in clinical settings, pending compliance with regulatory standards. Collaboration with domain experts ensures the system's alignment with clinical needs, while continual refinement based on feedback and advancements in the field drives ongoing improvement.
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DOI:
10.17148/IJARCCE.2024.134154
