Research Article
Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0
Adebayo Rotimi Philip*
Issue:
Volume 13, Issue 4, August 2024
Pages:
59-77
Received:
1 July 2024
Accepted:
24 July 2024
Published:
15 August 2024
Abstract: Much research has been done on the classification of the food101 dataset, but much of this research which achieved an accuracy score of more than 90% explores heavyweight architecture such as EfficientNetB7, Visual Geometry Group19, ResNet-200, Inception v4, DenseNet-201, ResNeXt-101, MobileNet v3 and many more. This study explores the classification of the Food101 dataset using the EfficientNetB0 architecture, a lightweight architecture. Compared to other popular CNN architecture, EfficientNetB0 has relatively small parameters, which makes it computationally efficient and suitable for deployment on resource-constraint environments. The research aims to balance model accuracy and computational efficiency, addressing the need for resource-constrained environments. Five experiments were conducted while varying the number of fine-tuned layers. Results demonstrate that the fine-tuned EfficientNetB0 model achieves an accuracy score of accuracy score of 97.54%, Top_k_categorical accuracy of 99.89%, precision of 98.21%, and recall of 97.02% in just 5 epochs. This research will significantly contribute to the field of transfer learning by developing specialized models that excel in target tasks. Besides, it will advance dietary monitoring, food logging, and health-related technologies, enabling more accessible and practical solutions for consumers. However, the optimal number of layers to fine-tune for achieving perfect accuracy with EfficientNetB0 remains uncertain. It often involves trial and error to determine the best configuration for optimal results, presenting an opportunity for future research.
Abstract: Much research has been done on the classification of the food101 dataset, but much of this research which achieved an accuracy score of more than 90% explores heavyweight architecture such as EfficientNetB7, Visual Geometry Group19, ResNet-200, Inception v4, DenseNet-201, ResNeXt-101, MobileNet v3 and many more. This study explores the classificati...
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Research Article
Development of an Expert System for Diagnosing Musculoskeletal Disease
Issue:
Volume 13, Issue 4, August 2024
Pages:
78-93
Received:
10 August 2024
Accepted:
28 August 2024
Published:
11 September 2024
DOI:
10.11648/j.ijiis.20241304.12
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Views:
Abstract: Musculoskeletal diseases (MSDs), encompasses various conditions affecting muscles, bones, tendons, ligaments, and joints, resulting to pain, inflammation, and limited mobility, significantly impacting individuals' quality of life. Diagnosing these diseases poses a challenge for healthcare professionals due to symptom similarities with other conditions. To address this, the development of expert systems tailored for musculoskeletal diagnosis has emerged as a promising approach to enhance clinical decision-making and improve patient outcomes. This study aims at developing and evaluating an expert system for musculoskeletal disease diagnosis, by leveraging a knowledge base containing information on common musculoskeletal diseases and symptoms. The system utilized a combination of rule-based and machine learning techniques to provide diagnostic recommendations to physicians. Comparative analysis with experienced physicians, using a dataset of patients with known musculoskeletal diseases, revealed the expert system’s diagnostic accuracy of 92%, recall of 98%, Precision of 91%, F1-Score of 94% and a quicker diagnosis compared to physicians. Additionally, the system demonstrated ease of use and user-friendliness. This project focuses on predictive algorithms, leveraging expert systems dating back to the 1970s, emulating human expert decision-making, particularly in disease diagnosis. The development of an expert system for musculoskeletal disease diagnosis symbolizes the convergence of medical expertise, computer science, and artificial intelligence. By integrating machine learning, natural language processing, and decision support systems, these expert systems have the potential to revolutionize musculoskeletal healthcare delivery. In conclusion, our results show that expert systems hold promise in transforming clinical practice and improving patient outcomes in musculoskeletal healthcare through interdisciplinary collaboration and continuous innovation.
Abstract: Musculoskeletal diseases (MSDs), encompasses various conditions affecting muscles, bones, tendons, ligaments, and joints, resulting to pain, inflammation, and limited mobility, significantly impacting individuals' quality of life. Diagnosing these diseases poses a challenge for healthcare professionals due to symptom similarities with other conditi...
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