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

TL;DR

This paper introduces SkullNetV1, a CNN combined with lazy learning for automatic classification of skull fractures in CT images, achieving high accuracy and efficiency in multi-label fracture detection.

Contribution

The paper presents a novel CNN model called SkullNetV1 that integrates lazy learning for improved multi-label skull fracture classification from CT scans.

Findings

Achieved 88% subset accuracy in fracture classification.

F1 score of 93% indicates high precision and recall.

AUC values ranged from 0.89 to 0.98, demonstrating strong discriminative ability.

Abstract

Classification of skull fracture is a challenging task for both radiologists and researchers. Skull fractures result in broken pieces of bone, which can cut into the brain and cause bleeding and other injury types. So it is vital to detect and classify the fracture very early. In real world, often fractures occur at multiple sites. This makes it harder to detect the fracture type where many fracture types might summarize a skull fracture. Unfortunately, manual detection of skull fracture and the classification process is time-consuming, threatening a patient's life. Because of the emergence of deep learning, this process could be automated. Convolutional Neural Networks (CNNs) are the most widely used deep learning models for image categorization because they deliver high accuracy and outstanding outcomes compared to other models. We propose a new model called SkullNetV1 comprising a novel CNN by taking advantage of CNN for feature extraction and lazy learning approach which acts as a classifier for classification of skull fractures from brain CT images to classify five fracture types. Our suggested model achieved a subset accuracy of 88%, an F1 score of 93%, the Area Under the Curve (AUC) of 0.89 to 0.98, a Hamming score of 92% and a Hamming loss of 0.04 for this seven-class multi-labeled classification.