Deep Learning-Based Framework for Accurate Target Detection in Medical Imaging: Enhancing Diagnostic Precision Through AI Integration

Abstract

When problems with the brain, known as intracranial hemorrhaging, go undiagnosed or untreated for long periods of time, the result may be devastating. Among the most significant ways to diagnose cerebral hemorrhages is via CT imaging. It takes a lot of expertise and time to properly analyze and diagnose CT scans because of the massive volumes of information they carry. Thus, AI methods aid radiologists in their diagnostic decision-making by providing an automated process for assessing CT images to provide a diagnosis with high accuracy. Three different systems, each with its own set of procedures and materials, are suggested here for the quick diagnosis of cerebral hemorrhages using CT scans; each of these systems comprises more than one network. Three pretrained deep learning models—AlexNet, ResNet-50, and GoogLeNet—propose the first system. Step one of the second hybrid system proposal is the feature map extraction models (GoogLeNet, ResNet-50, and AlexNet), and step two is the feature map classification technique (SVM). Moreover, because pre-trained models were readily available, we used the transfer learning approach to incorporate our convolutional network's linear classifier into an existing model; the outcome was far more encouraging.

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