ENHANCING DEEP LEARNING MODELS WITH HYBRID NEURAL ARCHITECTURES FOR IMAGE CLASSIFICATION

Abstract

The ongoing worldwide health disaster because of the unconventional covid19 (COVID-19) has emphasized the pressing need for rapid and price-effective diagnostic techniques. Traditional polymerase chain response trying out is time-ingesting and costly, prompting researchers to discover synthetic intelligence (AI) solutions for quick analysis. This takes a examine and provides a singular method using chest X-ray scans (CXRs) and an up-to-date VGG16 convolutional neural network (VGG16-CNN) to classes COVID-19, pneumonia, and everyday instances from public datasets. By using image cropping techniques and resizing, the have a look at completed an accuracy of 97.50% for more than one classification and a remarkable ninety-nine.76% for binary type, demonstrating the model's performance with a discounted parameter rely from approximately 138 million to round 40 million. In addition to breathing conditions, this studies additionally addresses the crucial vicinity of mind tumor diagnosis, which stays a chief purpose of mortality international.

Early detection is important for effective treatment, but conventional biopsy techniques are frequently invasive and now not executed previous to surgical operation. This portray introduces a hybrid CNN shape for classifying 3 varieties of mind tumors the use of magnetic resonance imaging (MRI) scans. By combining a pre-educated Google-Net model with guide vector tool (SVM) magnificence and a finely tuned Google-Net with an easy-max classifier, the approach completed an accuracy of 90-eight.1% for tumor popularity. Overall, this has a look at highlights the functionality of hybrid neural architectures in improving deep gaining knowledge of models for image class, imparting good sized advancements in clinical diagnostics for each breathing sicknesses and thoughts tumors. The findings underscore the importance of AI in enhancing detection strategies and in the end saving lives thru early intervention.