RETRACTED ARTICLE: Parkinson’s disease
         detection using modified ResNeXt deep learning model from brain MRI images

Balnarsaiah, Battula; Nayak, B. Ashok; Sujeetha, G. Spica; Babu, B. Surendra; Vallabhaneni, Ramesh Babu

doi:10.1007/s00500-023-08535-9

Cited by 8 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The addition of residual structures allows for the learning of more intricate features without significantly increasing the computational burden. The residual structure essentially creates a form of memory in the model, allowing it to learn from previously seen data and thus improving the model’s capacity to generalize [ 34 ]. Furthermore, the residual structure mitigates the vanishing gradient problem, enabling the model to learn deeper representations without converging prematurely.…”

Section: Methodsmentioning

confidence: 99%

Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP

Hong,

Wu,

Zhu

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Brain tumors pose a significant threat to health, and their early detection and classification are crucial. Currently, the diagnosis heavily relies on pathologists conducting time-consuming morphological examinations of brain images, leading to subjective outcomes and potential misdiagnoses. In response to these challenges, this study proposes an improved Vision Transformer-based algorithm for human brain tumor classification. To overcome the limitations of small existing datasets, Homomorphic Filtering, Channels Contrast Limited Adaptive Histogram Equalization, and Unsharp Masking techniques are applied to enrich dataset images, enhancing information and improving model generalization. Addressing the limitation of the Vision Transformer’s self-attention structure in capturing input token sequences, a novel relative position encoding method is employed to enhance the overall predictive capabilities of the model. Furthermore, the introduction of residual structures in the Multi-Layer Perceptron tackles convergence degradation during training, leading to faster convergence and enhanced algorithm accuracy. Finally, this study comprehensively analyzes the network model’s performance on validation sets in terms of accuracy, precision, and recall. Experimental results demonstrate that the proposed model achieves a classification accuracy of 91.36% on an augmented open-source brain tumor dataset, surpassing the original VIT-B/16 accuracy by 5.54%. This validates the effectiveness of the proposed approach in brain tumor classification, offering potential reference for clinical diagnoses by medical practitioners.

show abstract

Section: Methodsmentioning

confidence: 99%

Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP

Hong,

Wu,

Zhu

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The pre-trained model is employed with parameters of 13 convolutional layers and a fully connected layer. The ResNeXt is a development of the deep residual network [27]. It is a simple network for image classification and it is made up of repeated blocks of the layers.…”

Section: Feature Extraction Using a Pre-trained Modelmentioning

confidence: 99%

Detection of diseases in rice leaf using convolutional neural network with transfer learning based on ResNeXt

Veeramreddy,

Gnanasekaran,

Kunchala

2024

IJECE

View full text Add to dashboard Cite

Rice is the most cultivated crop in the agricultural field and is a major food source for more than 60% of the population in India. Occurrence of disease in rice leaves, majorly affects the rice quality, production and also the farmers’ income. Nowadays, new variety of diseases in rice leaves are identified and detected periodically throughout the world. Manual monitoring and detection of plant diseases proves to be time consuming for the farmer and also a costly affair for using chemicals in the disease treatment. In this paper, a deep learning method of convolutional neural networks (CNN) with a transfer learning technique is proposed for the detection of a variety of diseases in rice leaves. This method uses the ResNeXt network model for classifying the images of disease-affected plants. The proposed model’s performance is evaluated using accuracy, precision, recall, F1-score and specificity. The experimental results of ResNeXt model measured for accuracy, precision, recall, F1-score, and specificity, are respectively 99.22%, 92.87%, 91.97%, 90.95%, and 99.05%, which proves greater accuracy improvement than the existing methods of SG2-ADA, YOLOV5, InceptionResNetV2 and Raspberry Pi.

show abstract

Zebra based optimal deep learning for Parkinson’s disease detection using brain MRI images

2024

Multimed Tools Appl

View full text Add to dashboard Cite

RETRACTED ARTICLE: Parkinson’s disease detection using modified ResNeXt deep learning model from brain MRI images

Cited by 8 publications

References 14 publications

Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP

Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP

Detection of diseases in rice leaf using convolutional neural network with transfer learning based on ResNeXt

Zebra based optimal deep learning for Parkinson’s disease detection using brain MRI images

Contact Info

Product

Resources

About