Architectural Model for a Tuberculosis Diagnostic System Using CNNs for Chest X-ray Image Analysis
Keywords:
Tuberculosis, CNN, Datasets, Machine LearningAbstract
One of the major threats to global health in the past decades is Tuberculosis (TB), which necessitates timely and precise diagnosis to guarantee timely handling, and diminish transmission. One of the most extensive means of detecting TB has been Chest X-Ray imaging, which requires manual interpretation. But this methodology is vulnerable to proclivity and delays, in particular where resource limitation is a factor. This paper presents an architectural framework for an automated Tuberculosis Diagnostic System that utilizes Convolutional Neural Networks (CNNs) for analyzing chest X-ray images. The proposed framework aims to augment diagnostic competence, accurateness, and scalability by automating the attribute extraction and classification process, thus providing for a persistent decision-support device for medical practitioners. To aid up the process, Transfer Learning techniques are added to improve definitiveness, particularly in case of petite, unbalanced datasets. The proposed system exhibits major potential to trim down diagnostic fallacy, accelerate the discovery of TB, and advance improvements of patients, predominantly in areas with restricted access to skilled radiologists. This paper provides groundwork for the development of AI-enabled apparatus that can change TB analysis, building a quicker, more precise, and available globally.
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