<![CDATA[Owing to the significance of early diagnosis and proper clinical decision-making, medical image analysis has now become an essential part of modern healthcare systems. Traditional image analysis techniques weaken for complex patterns, variability in medical data and a high diagnostic accuracy need We therefore propose in this paper a new deep learning-based framework for adaptive feature learning and improved diagnostic accuracy. We then present a convolutional neural network (CNN) based hybrid architecture framed with an adaptive feature learning strategy that learns suitable spatial and semantic characteristics for fine-tuning dynamically. The proposed method will help the model more accurately discover small details in medical images (e.g., lesions, tumors and unusual tissue structures). In addition, a feature fusion strategy is applied to combine multiscale representations which improve the robustness of multiple imaging modalities (MRI, CT and X-ray). Besides that the model implements various optimizations: batch norm, dropout regularization and adaptive learning rate scheduling. Extensive experiments conducted on benchmark medical imaging datasets confirmed the efficacy of the proposed method. The result shows that it outperforms any previous existing state-of-the-art methods by a considerable margin on the accuracy, precision, recall and f1-score metrics. This framework also obtained a greater performance on generalizability, and decreased sensitivity to noise-induced fluctuations over quality of picture changes. We demonstrate that the incorporation of tray-like adaptive feature learning through deep neural networks can lead to substantial improvements on image such as medical imaging. This paper provides a foundation for the development of intelligent, trustworthy and scalable AI based health care systems that assist clinicians in improving decision making with high correct classification rates as fast as possible.]]>
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