Hybrid deep learning model for identifying the cancer type

Despite current advances, cancer remains one of the biggest health challenges globally, and diagnosis must be made earlier to begin treatment. In this work, we introduce a hybrid deep learning-based framework for accurate cancer type and subtype identification by using pre-trained convolutional neural networks, custom deep learning networks, and traditional machine learning classifiers. I have achieved accurate results on more complex cancer datasets using advanced architectures of CNN + LSTM and attention-based models, along with the pre-trained models of VGG19, Xception, and AmoebaNet. Model reliability and interpretability are further improved using ensemble techniques such as confidence-based and XOR fusion. Experimental results in multiple multimodal datasets demonstrate the effectiveness of our hybrid approach by improving precision, recall, and F1 scores in various types of cancer. However, they have promising results and remain challenging to deploy for rare cancer subtypes or explain to gain clinical adoption. The proposed framework provides a basis for personalized cancer by developing machine learning innovations to advance precision medicine.