Authors - Khushi Mantri, Abhishek Masne, Shruti Patil, Girish Mundada Abstract - In medical diagnostics, identifying bone fractures is a crucial task that is traditionally dependent on radiologists deciphering X-ray pictures. However, human factors like experience or exhaustion can occasionally cause delays or inaccuracies in diagnosis. The construction of an automated system for bone fracture identification utilizing Convolutional Neural Networks (CNN), a deep learning method that performs especially well in picture processing, is examined in this research. With the use of a tagged dataset of X-ray pictures, the suggested method can efficiently and accurately detect fractures. Prior to feature extraction using CNN layers which are trained to distinguish between fractured and non-fractured bones the images are pre-processed to improve clarity. In order to assist medical practitioners in making prompt, correct judgments, the final classification attempts to increase diagnostic accuracy while decreasing the amount of time needed for analysis. The potential of incorporating machine learning into healthcare to lower diagnostic errors and enhance patient outcomes is also discussed in this overview paper, which includes examines recent developments in CNN-based medical picture categorization.
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