Accurate and real-time polyp segmentation plays a vital role in the early detection of colorectal cancer. However, existing methods often rely on computationally expensive backbones, single attention mechanisms, and suboptimal feature fusion strategies, limiting their practicality in real-world scenarios. In this work, we propose a lightweight yet effective deep learning framework that strikes a balance between precision and efficiency through a carefully designed architecture. Specifically, we adopt a MobileNetV4-based hybrid backbone to extract rich multi-scale features with significantly fewer parameters than conventional backbones, making the model well-suited for resource-constrained cl... More >

Brain tumor detection (BTD) is a crucial task, as early detection can save lives. Medical professionals require visual intelligence assistance to efficiently and accurately identify brain tumors. Conventional methods often result in misrecognition, highlighting a critical research gap. To address this, a novel BTD system is proposed to predict the presence of a tumor in a given MRI image. The system leverages a convolutional neural network (CNN) architecture, combined with a multi-layer perceptron (MLP) for feature extraction and understanding complex pixel patterns. An extensive ablation study was conducted to empirically analyze and identify the optimal model for the task. The findings dem... More >