A Deep Learning Framework for Efficient Bone Cancer Detection and Classification

Deep learning is now a game-changing technology with applications in medical imaging and is especially showing great promise in the diagnosis of malignancies. In summary, bone cancer is a rare but challenging disease that requires accurate diagnosis for prompt treatment. Biopsy is time-consuming and subjective, being heavily dependent on radiological examination. The first is applying explainable AI techniques to improve the interpretability and performance of trained bone cancer detection models for clinical practice; the second is using CNNs to increase the efficiency and accuracy of bone cancer detection and develop a highly accurate and reliable LSTM model for automatic detection of bone cancer from time-sequence images. In the scenario of CNN architectures, improvements are made by employing cutting-edge methodologies like transfer learning, data augmentation, or optimization methodologies that guarantee better feature extraction and classification. To more efficiently utilize sequential information from medical images, hybrid CNN-LSTM architectures are also proposed to learn not only intra-frame features (of a single image) from CNN but also related sequential contexts across timeframes using LSTM in the movie time series data of bone tumors. Additionally, breeds of explainable AI methods like Grad-CAM, SHAP, LIME are used to disclose model predictions and build trust, and encourage adoption by clinicians.