Gopi Battineni

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study’s commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

Retinal blood vessels are affected by a variety of eye diseases, including hypertensive retinopathy (HR) and diabetic retinopathy (DR). A person with HR needs to be sure to check their eyes regularly, which requires the use of computer vision methods to analyze images of the back of the eye and help ophthalmologists automatically. Automated diagnostic systems are useful for diagnosing different retinal diseases for ophthalmologists and patients who need to establish an automated HR detection and classification system using retinal images. In this work, a sliding band filter was used to improve the back-of-the-eye images and small convex regions to develop an automated system for detecting and classifying HR gravity levels. An image classification with improved wolf optimization along Bayes algorithm was conducted. The current model was tested using the publicly available dataset, and its results were compared to existing models. The results mentioned that the model-improved Naïve Bayes model classified the different HR severity levels on the optimized features and produced a maximum accuracy of 100% while being compared to other classifiers.