Titlebook: Data-Driven Clinical Decision-Making Using Deep Learning in Imaging; M. F. Mridha,Nilanjan Dey Book 2024 The Editor(s) (if applicable) and

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Eisen(II)-hydrocarbonat Fe(HCO3)2,the gradient problem, resulting in an optimized and efficient training process. Our proposed model outperformed all existing models including the SOTA model, with an accuracy of 89.95%, precision of 91.42%, recall of 88.84%, F1 of 89.68%, and specificity of 95.98%.

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,Advancing Brain Tumour Detection: Transfer Learning-Based Approach Fused with Squeeze-and-Excitatioenchmarked with the previous seven state-of-the-art (SOTA) models on the same dataset. Our proposed techniques obtained the best results for both the validation and testing datasets. On the validation data of the MRI brain tumour, we achieved the highest results, with an accuracy of 95.92%, precision of 95.89%, recall of 95.24% and AUC of 99.00%.

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Enhancing Breast Cancer Detection Systems: Augmenting Mammogram Images Using Generative Adversarialbuted to the labor-intensive curation and labeling of images, coupled with privacy concerns, serves as a driving force behind investigating GANs as a potential solution. This exploration aims to address the challenge of obtaining a more extensive and diverse dataset, essential for the robust training of breast cancer detection systems.