Abstract:
Small bowel obstruction (SBO) is a common and potentially fatal condition that occurs with the
partial or complete blockage of the small bowel, hindering the transport of digestive content. The
most common causes are adhesions following surgery, hernias, tumors, Crohn's disease, and
intussusception. The patient experiences clinical presentation of pain in the abdomen, vomiting,
distension, and constipation. Possessing an accurate and timely diagnosis is critical in preventing
complications such as ischemia or perforation; appropriate investigations including abdominal X rays and CT scans are therefore necessary. With the advent of deep learning techniques in medical
image analysis, there has been increasing encouragement in improving diagnostic accuracy. Earlier
approaches were mostly focused on either single-feature extraction methods or individual
classifiers. We propose a hybrid deep learning approach combining CNN and Histogram of
Oriented Gradients (HOG) for feature extraction, followed by Support Vector Machines (SVMs)
for classification. This hybrid approach combines both worlds in traditional machine learning and
deep learning techniques. The accuracy achieved by the proposed model in classifying obstructed
from non-obstructed cases is 97%. Our model was also compared against some renowned deep
learning models, including InceptionV3, VGG16, and AlexNet, for the sake of performance
evaluation. Experimental results showed that the hybrid model surpassed these state-of-the-art
models in classification accuracy, thus indicating its potential application in clinical diagnostic
workflows.
In essence, this study has built a robust and reliable automated diagnostic system for detecting and
classifying SBO from abdominal X-ray images. The resultant findings are very much contributory
to AI-assisted diagnostic imaging and greatly support reducing the burden of work on health
professionals while ensuring timely and accurate medical interventions.
Keywords: Small Bowel Obstruction, Deep Learning, Convolutional Neural Networks (CNN),
Support Vector Machine (SVM), Abdominal X-ray Images, Automated Diagnosis, Classificatio
