A Robust Model for Optimum Medical Image Contrast Enhancement and Tumor Screening

The use of medical imaging techniques have improved the correctness of disease screening and diagnosis. But, the quality of these images is greatly affected by real-time factors such as the type of machinery used, the position of a patient, the intensity of light, etc. The poorly maintained machines, incorrect positioning of patients, and inadequate intensity of light lead to low contrast and poor-quality medical images that work as hindrances in examining medical images. Thus, there is a need to upgrade the features of medical images. Researchers applied histogram equalization for contrast enhancement. However, it improves the visual appearance of medical images but faces the difficulties of over-enhancement, noise, and undesirable artifacts. Also, these techniques report low accuracy in tumor detection. Therefore, we propose an efficient model for medical image contrast enhancement and correct tumor prediction. The model performs segmentation, weighted distribution, gamma correction, and filtering to improve the visual appearance of MRI images. Further, it employs the optimum feature extraction for the correct detection of regions infected with tumors. Furthermore, findings obtained in a simulated environment demonstrate that our proposed model outperforms current models.

Keywords: Automatic, Adaptive gamma correction, Brightness preservation, Brain tumor detection, Contrast enhancement, Convolutional neural network, Deep learning, Entropy, Gray level co-occurrence matrix, Histogram equalization, Homomorphic filtering, Image classification, Model, Medical resonance imaging, Machine learning, Medical imaging, Optimum, Peak signal to noise ratio, Threshold, Tumor, Weighted distribution.