Abstract:

The increasing impact of machine learning on medical sciences is revolutionizing diagnostic procedures and clinical decision-making. Specifically, the incorporation of smart algorithms into healthcare systems is making medical diagnostics faster, more precise, and efficient. Nevertheless, conventional tumor detection in radiological imaging is usually time-consuming, dependent on the skills of radiologists, and subject to human error, which might result in misdiagnosis or delayed treatment. For mitigating theses challenges, the current work formulates a self-contained machine-learning framework to identify tumors via RI-CNNs, an application of deep learning specifically customized to process medical images. In its proposed procedure, the scheme encompasses image pre-processing, feature selection, and classification stages whereby RI-CNNs are used for detecting as well as segmenting tumor tissues with high accuracy. Through automating the detection process, the framework decreases reliance on human analysis and improves diagnostic consistency across a wide range of clinical environments. The use of RI- CNNs has proven substantial improvement in tumor detection accuracy, sensitivity, and specificity compared to traditional image analysis techniques. Experimental findings show that the envisioned system not only speeds up diagnostic processes but also enhances the quality of patient treatment in general with early and reliable tumor identification. The results show that machine learning, more specifically CNN-based architectures, has tremendous potential in transforming radiological procedures and assisting healthcare experts to make accurate, timely, and effective diagnoses.