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The extremely low tumor prevalence in the general population makes accurate image-based tumor identification and classification a significant obstacle for medical researchers. Timely therapies and better patient outcomes depend on the capacity to identify cancers in anomalous circumstances. This research seeks to solve the problem by identifying and categorizing brain tumors in difficult cases using deep learning (DL) methods. Hence this paper, Dilated Convolution and YOLOv8 Feature Extraction Network (DC-YOLOv8FEN) is proposed to improve tumor detection accuracy. To begin, the feature extraction network (FEN) is improved with the help of the self shuffle attention (SSA), and the feature map is maximized with the help of vision transformer block. Secondly, a dual feature pyramid network (DFPN) is built to provide more discriminative data for dynamic sparse attention mechanism to extract features from the shallow network and top-down routes to direct the following network modules to fuse features. In addition, the DC-YOLOv8FEN uses a position regression loss function and adds Wise-IoU’s dynamic non-monotonic focus mechanism to make it even better. Based on the experimental results, the proposed method achieved precision of 95.2%, recall rate of 90.5%, accuracy of 99.5% and F1-score of 96.5% which evinces the outstanding performance compared with the existing methods. Radiologists may use this technology for better identification and diagnose brain tumors in real-time medical imaging systems, which improves patient outcomes and reduces diagnostic delays.
Abraham et al. (Wed,) studied this question.