A COMPREHENSIVE REVIEW OF MEDICAL IMAGING DATA USING DEEP LEARNING TECHNIQUES FOR CANCER DETECTION
DOI:
https://doi.org/10.4314/njt.2026.6052SIKeywords:
Deep Learning, Cancer, CNN, MRI, OCRAbstract
Cancer remains a significant public health issue due to the uncontrolled growth of abnormal cells that can harm normal tissues. Early detection is vital for improving treatment outcomes and survival rates, yet over the past decade, more than 0.4 million new cancer cases were diagnosed, with over 0.17 million fatalities. Current diagnostic methods, such as CT, MRI, PET, ultrasound, and biopsies, often struggle to identify subtle signs of early-stage cancer and do not fully leverage the available data from medical imaging and histopathology. This research aims to summarize studies that use deep learning algorithms on medical imaging data for early-stage cancer diagnosis. By integrating diverse data types, including imaging and histopathology, we seek to empower clinicians with enhanced diagnostic tools that reduce false positives and negatives. Ultimately, the goal is to improve patient treatment options and survival rates. Addressing the challenges of early cancer detection and treatment remains crucial in modern medicine. The development of innovative diagnostic technologies has the potential to improve the effectiveness of cancer treatments, thereby reducing mortality rates associated with the disease. Traditional clinical diagnostic methods have had limited success in detecting early-stage cancers because of the early subtle nature of their manifestations. Using Deep Learning to analyze large volumes of medical images that are taken from patients diagnosed with many different forms of cancer to develop a system that will allow clinicians to take advantage of medical images to make an early diagnosis of cancer, thereby enhancing the quality of care for patients. This is a literature review of medical imaging modalities commonly used in clinics, including X-ray, CT, PET, and MRI, which are commonly insufficient to detect early-stage cancer using Machine Learning, and there has been limited research on analyzing Optical Coherence Tomography medical images in conjunction with a Hybrid Convolutional-Recurrent Neural Network.
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