PDF Deep Learning in Tumor Detection: Advancing Accuracy and Efficiency in Cancer Diagnosis

Abstract

Precise early identification of cerebral neoplasms can markedly enhance patient prognosis. Conventional diagnostic techniques, including manual analysis of medical pictures, are labor-intensive and susceptible to subjective bias. This study employs deep learning methodologies, specifically Convolutional Neural Networks (CNNs) and the YOLO (You Only Look Once) model, to improve the precision of brain tumor diagnosis in MRI scans. A dataset including 961 labeled MRI images was employed, resized to 416 × 416 pixels, and partitioned into training, validation, and test sets. A multi-class CNN model was created for classification, and YOLOv11 was employed for real-time detection and classification. Performance evaluation utilized mean Average Precision (mAP), precision, and recall, yielding 95.4% mAP, 91.1% precision, and 93.2% recall using YOLOv11, indicating substantial clinical potential. Challenges remain, including data variability and model interpretability, underscoring the need for models that improve transparency and strengthen NLP techniques. The NLP integration involves using the Gemini large language model (LLM) to automatically generate comprehensive diagnostic reports. Specifically, the NLP system analyzes outputs from the CNN-based image classification, combined with patient notes, to produce detailed, context-aware medical reports, thereby enhancing clinical diagnostics. These findings endorse the advancement of refined deep-learning models for expedited and precise tumor detection, facilitating real-time clinical applications.