Optimizing U-Net architecture with feed-forward neural networks for precise Cobb angle prediction in scoliosis diagnosis

Jamaludin, Mohamad Iqmal and Gunawan, Teddy Surya and Karupiah, Rajandra Kumar and Zabidi, Suriza Ahmad and Kartiwi, Mira and Zakaria@Mohamad, Zamzuri (2023) Optimizing U-Net architecture with feed-forward neural networks for precise Cobb angle prediction in scoliosis diagnosis. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 11 (3). pp. 883-895. ISSN 2089-3272

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Abstract

In the burgeoning field of Artificial Intelligence (AI) and its notable subsets, such as Deep Learning (DL), there is evidence of its transformative impact in assisting clinicians, particularly in diagnosing scoliosis. AI is unrivaled for its speed and precision in analyzing medical images, including X-rays and computed tomography (CT) scans. However, the path does not lack obstacles. Biases, unanticipated outcomes, and false positive and negative predictions present significant challenges. Our research employed three complex experimental sets, each focusing on adapting the U-Net architecture. Through a nuanced combination of feed-forward neural network (FFNN) configurations and hyperparameters, we endeavored to determine the most effective nonlinear regression model configuration for predicting the Cobb angle. This was done with the dual purpose of reducing AI training time without sacrificing predictive accuracy. Utilizing the capabilities of the PyTorch framework, we meticulously crafted and refined the deep learning models for each of the three experiments, focusing on an FFFN dropout rate of p=0.45. The Root Mean Square Error (RMSE), the number of epochs, and the number of nodes spanning three hidden layers in each FFFN were utilized as crucial performance metrics while a base learning rate of 0.001 was maintained. Notably, during the optimization phase, one of the experiments incorporated a learning rate scheduler to protect against potential pitfalls such as local minima and saddle points. A judiciously incorporated Early Stopping technique, triggered between the patience range of 5-10 epochs, ensured model stability as the Mean Squared Error (MSE) plateau loss approached approximately 1. Consequently, the model converged between 50 and 82 epochs. We hypothesize that our proposed architecture holds promise for future refinements, conditioned on assiduous experimentation with an array of medical deep learning paradigms.

Item Type:	Article (Journal)
Additional Information:	5588/108082
Uncontrolled Keywords:	Artificial Intelligence, Deep Learning, U-Net Architecture, Scoliosis Diagnosis, Cobb Angle Prediction, PyTorch Framework
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Medicine Kulliyyah of Medicine > Department of Department of Orthopaedics, Traumatology & Rehabilitation Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	14 Nov 2023 10:03
Last Modified:	23 Jan 2024 09:05
URI:	http://irep.iium.edu.my/id/eprint/108082

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