Developing New Fully Connected Layers for Convolutional Neural Networks with Hyperparameter Optimization for Improved Multi-Label Image Classification

Tamás Katona (), Gábor Tóth, Mátyás Petró and Balázs Harangi
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Tamás Katona: Doctoral School of Informatics, University of Debrecen, 4032 Debrecen, Hungary
Gábor Tóth: Department of Laboratory Medicine, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
Mátyás Petró: Department of Radiology, Medical Imaging Insitute, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
Balázs Harangi: Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary

Mathematics, 2024, vol. 12, issue 6, 1-16

Abstract: Chest X-ray evaluation is challenging due to its high demand and the complexity of diagnoses. In this study, we propose an optimized deep learning model for the multi-label classification of chest X-ray images. We leverage pretrained convolutional neural networks (CNNs) such as VGG16, ResNet 50, and DenseNet 121, modifying their output layers and fine-tuning the models. We employ a novel optimization strategy using the Hyperband algorithm to efficiently search the hyperparameter space while adjusting the fully connected layers of the CNNs. The effectiveness of our approach is evaluated on the basis of the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metric. Our proposed methodology could assist in automated chest radiograph interpretation, offering a valuable tool that can be used by clinicians in the future.

Keywords: chest X-ray; multi-label classification; convolutional neural networks; deep learning; Hyperband optimization; VGG; ResNet; DenseNet; AUC-ROC (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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