Dataset Information

Optimized MobileNetV3: a deep learning-based Parkinson's disease classification using fused images.

ABSTRACT:

Background and objective

Parkinson's disease (PD) is a progressive neurological condition that manifests motor and non-motor symptoms. Early in the course of the disease, PD patients frequently experience vocal difficulties. In the beginning, preprocessing procedures were used with multi-focus image fusion to enhance the quality of input images. It is essential to diagnose and treat PD early to ensure that patients live healthy and productive lives.

Methods

Tremors, rigidity in the muscles, slow movement, difficulty balance, and other psychological symptoms are some of the disease's symptoms. One of the critical mechanisms supporting PD identification and assessment is the dynamics of handwritten records. Several machine-learning techniques have been researched for the early detection of this disease. Yet the main problem with most of these manual feature extraction methods is their poor performance and accuracy.

Results

This cannot be acceptable when discovering such a chronic condition. For this purpose, a powerful deep learning model is suggested to help with the early diagnosis of Parkinson's disease. Therefore, we proposed MobileNetV3-based classification. To enhance the classification performances even more, the MobileNetV3-based approach was optimized by the Improved Dwarf Mongoose Optimization algorithm (IDMO).

Conclusion

The Pyramid channel-based feature attention network (PCFAN) chooses the critical features. The efficiency of the approaches is tested using the PPMI and NTUA datasets. Our proposed approach obtains 99.34% accuracy, 98.53% sensitivity, 97.78% specificity, and 99.12% F-score compared to previous methods.

SUBMITTER: Pechetti S

PROVIDER: S-EPMC10702996 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Optimized MobileNetV3: a deep learning-based Parkinson's disease classification using fused images.

Pechetti Sukanya S Rao Battula Srinivasa BS

PeerJ. Computer science 20231127

<h4>Background and objective</h4>Parkinson's disease (PD) is a progressive neurological condition that manifests motor and non-motor symptoms. Early in the course of the disease, PD patients frequently experience vocal difficulties. In the beginning, preprocessing procedures were used with multi-focus image fusion to enhance the quality of input images. It is essential to diagnose and treat PD early to ensure that patients live healthy and productive lives.<h4>Methods</h4>Tremors, rigidity in th ...[more]

PMID: 38077616

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Project description:IntroductionInfectious keratitis is a vision threatening disease. Bacterial and fungal keratitis are often confused in the early stages, so right diagnosis and optimized treatment for causative organisms is crucial. Antibacterial and antifungal medications are completely different, and the prognosis for fungal keratitis is even much worse. Since the identification of microorganisms takes a long time, empirical treatment must be started according to the appearance of the lesion before an accurate diagnosis. Thus, we developed an automated deep learning (DL) based diagnostic system of bacterial and fungal keratitis based on the anterior segment photographs using two proposed modules, Lesion Guiding Module (LGM) and Mask Adjusting Module (MAM).MethodsWe used 684 anterior segment photographs from 107 patients confirmed as bacterial or fungal keratitis by corneal scraping culture. Both broad- and slit-beam images were included in the analysis. We set baseline classifier as ResNet-50. The LGM was designed to learn the location information of lesions annotated by ophthalmologists and the slit-beam MAM was applied to extract the correct feature points from two different images (broad- and slit-beam) during the training phase. Our algorithm was then externally validated using 98 images from Google image search and ophthalmology textbooks.ResultsA total of 594 images from 88 patients were used for training, and 90 images from 19 patients were used for test. Compared to the diagnostic accuracy of baseline network ResNet-50, the proposed method with LGM and MAM showed significantly higher accuracy (81.1 vs. 87.8%). We further observed that the model achieved significant improvement on diagnostic performance using open-source dataset (64.2 vs. 71.4%). LGM and MAM module showed positive effect on an ablation study.DiscussionThis study demonstrated that the potential of a novel DL based diagnostic algorithm for bacterial and fungal keratitis using two types of anterior segment photographs. The proposed network containing LGM and slit-beam MAM is robust in improving the diagnostic accuracy and overcoming the limitations of small training data and multi type of images.

Dataset Information

Optimized MobileNetV3: a deep learning-based Parkinson's disease classification using fused images.

Background and objective

Methods

Results

Conclusion

Publications

Optimized MobileNetV3: a deep learning-based Parkinson's disease classification using fused images.

Similar Datasets

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