Abstract:
Cancer, a leading cause of death worldwide, poses challenges due to late-stage detection
and inaccurate imaging techniques. Precise and effective screening is crucial for early
detection and treatment. Various low-cost and accurate imaging techniques are used, but
difficulties arise when experts struggle to interpret certain image areas, leading to missed
cancer diagnoses. To address this, computer-based software utilizing deep learning models
and algorithms has been developed. Traditional approaches have evolved into
computerized tools that analyze, diagnose, and predict symptoms. This study conducted a
comparative analysis of three detection models of different frameworks for binary and
multi-class image classification using image processing techniques. Despite their distinct
inputs, model network architecture Convolutional Neural Networks (CNN). The data were
split into sets: training, validation, and testing, and the evaluation involved learning curves
of training and validation loss and accuracy as well as a comparison of training, validation,
and test accuracies to check the model performances. The results showed the accuracy of
all models of predicting unknown images of cancerous or non-cancerous. The Difference
in input data and model learning rate could be the reason for variation in test results.
Computer language, python, and the platform, Pycharm IDE have been used for tasks
performed.
