Online Aid For Detecting Brain Tumor And Tuberculosis Using Deep Learning

Artificial intelligence(AI) is transforming the healthcare industry in a variety of ways, including disease diagnosis through medical imaging, increasing overall hospital efficiency, and so on. AI in health care will play a significant role in lowering death rates and thereby enhancing the nation’s overall medical infrastructure and standards. From its present valuation of $4.9 billion USD, the AI healthcare market is predicted to reach $45.2 billion USD by 2026. The absence of well-experienced medical experts and diagnosticians, as well as the expense and time required to diagnose and detect the illness, are major risk factors in particular underdeveloped countries and underrated areas. In developing countries across the world, the shortage of trained healthcare personnel can severely limit access to life-saving care. In the event of a positive diagnostic, the patient’s risk ultimately rises as time passes. As a result, we require an intelligent system capable of overcoming all of the aforementioned risks. Artificial intelligence (AI) may help alleviate the effect of the extreme shortage of trained healthcare personnel by taking over some of the diagnostic duties performed by them. Deep learning is shown to be superior at detecting diseases using X-rays, MRI scans, and CT scans, which could help doctors, diagnose patients faster and more accurately. The convolutional neural networks like Residual network (ResNet), MobileNetV2 are used for disease detection. These trained models are deployed in an application available to users all over the world even in low-resource areas, minimizing the requirement for an on-site certified diagnostic radiologist. In a better perspective, these applications can assist medical practitioners and diagnosticians in making more accurate clinical judgments, hence lowering the rate of medical error and increasing efficiency.