Hybrid contrast-limited adaptive histogram equalization and Deep Learning techniques for improving liver tumor detection

Deep Learning and advanced image processing can enhance the detection and prognosis of liver cancer using medical imaging, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. Liver cancer detection is a challenging task due to factors such as poor contrast, noise in imaging techniques, limited annotated datasets, and the complex characteristics of tumors. This study proposes a hybrid technique that combines Contrast-Limited Adaptive Histogram Equalization (CLAHE), Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Transfer Learning (TL) to improve the precision and accuracy of liver tumor detection. A conventional technique for image enhancement, CLAHE increases the contrast of medical images, making malignant tumors more apparent. CLAHE, however, does not provide a thorough tumor characterization; instead, it focuses on enhancing image quality. CNN is used to extract features, find and learn important patterns, such as edges, textures, and shapes that are pertinent to the diagnosis of tumors. Finally, TL utilizes pre-trained models (Inception V3) for classification, enabling the effective learning of tumor features and achieving high diagnostic precision with fewer computational resources. A hybrid approach combining CNN, GAN, and TL may give an integrated and effective solution for identifying and diagnosing liver tumors. The hybrid technique performed significantly better than independent DL approaches, achieving an accuracy of 93.3%, a sensitivity of 92.2%, a specificity of 94.5%, and an F1-score of 92.8%.