The Art of YOLOv8 Algorithm in Cancer Diagnosis using Medical Imaging

Cancer continues to be a global health challenge, demanding innovative solutions to improve early detection and treatment outcomes. This research project harnesses the power of deep learning in the field of medical imaging to investigate the applicability of the YOLOv8 (You Only Look Once version 8) algorithm for diagnosing various cancer types, such as Acute Lymphoblastic Leukemia, Cervical, Lung, Colon, Oral, and Skin cancers. The YOLOv8 algorithm, renowned for its real-time object detection prowess, represents a promising candidate for automating the identification and classification of cancerous regions within medical images. This study encompasses a comprehensive methodology, starting with the collection and preprocessing of diverse and well-annotated medical image datasets. The YOLOv8 algorithm is then fine-tuned and trained on these datasets, capitalizing on its object detection capabilities to discern cancerous lesions. The model's performance undergoes a comprehensive evaluation using established metrics, guaranteeing its dependability and precision within a clinical setting. The findings of this study have the potential to offer insightful information on YOLOv8. By bridging the gap between cutting-edge deep learning technology and clinical practice, this research project seeks to advance the field of medical imaging and provide a foundation for more precise, efficient, and accessible cancer detection methods. Ultimately, the goal is to enhance the early diagnosis of cancer, offering new possibilities for timely intervention and improved patient outcomes.