Abstract—The rapid ascent of deep learning in medical image analysis has been important in the remarkable advancements made in automated tumor identification. This study presents a novel convolutional neural network architecture for efficient tumor classification by integrating multi-scale fusion techniques with dual-path attention. The model employs sophisticated preprocessing techniques including Anisotropic Diffusion Filtering (ADF), Contrast Limited Adaptive Histogram Equalization (CLAHE), and SOBEL on 64×64×3 RGB input images that have already been preprocessed to enhance contrast and edge features. The Dual-Path Attention Block integrates Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) attention methods to document spatial and channel interdependence, making it a crucial component of the design. Multi-Scale Fusion Blocks enhance feature diversity and effectively capture local and global context by using parallel convolutional paths (1×1, 3×3 dilated, and 5×5). After three iterations of the design, the final prediction is made using a global average pooling layer, a lightweight classification head, and a softmax output layer. The model’s excellent accuracy and robustness in distinguishing between tumor and non-tumor regions, as shown by experimental results, making it a great fit for resource-constrained medical devices that need to be deployed in real-time.
 
Keywords—iris tumor detection, deep learning, attention mechanisms, medical image classification, process innovation, health system, illness
 
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