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Classification of skull fracture is a challenging task for both radiologistsand researchers. Skull fractures result in broken pieces of bone, which can cutinto the brain and cause bleeding and other injury types. So it is vital todetect and classify the fracture very early. In real world, often fracturesoccur at multiple sites. This makes it harder to detect the fracture type wheremany fracture types might summarize a skull fracture. Unfortunately, manualdetection of skull fracture and the classification process is time-consuming,threatening a patient's life. Because of the emergence of deep learning, thisprocess could be automated. Convolutional Neural Networks (CNNs) are the mostwidely used deep learning models for image categorization because they deliverhigh accuracy and outstanding outcomes compared to other models. We propose anew model called SkullNetV1 comprising a novel CNN by taking advantage of CNNfor feature extraction and lazy learning approach which acts as a classifierfor classification of skull fractures from brain CT images to classify fivefracture types. Our suggested model achieved a subset accuracy of 88%, an F1score of 93%, the Area Under the Curve (AUC) of 0.89 to 0.98, a Hamming scoreof 92% and a Hamming loss of 0.04 for this seven-class multi-labeledclassification.

Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach