Medical Image Classification for Disease Prediction with the aid of Deep Learning approaches

Kantheti, Bhargav; Javvaji, Mukesh Kumar

doi:10.1109/iciccs53718.2022.9788144

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Every human being has three basic remembrances: i) working memory, which is in charge of maintaining focus and concentration while processing data; ii) short-term storage was keeping information for durations up to certain limit; iii) long-term storage should be in charge of preserving the entire occurrences researchers encounter for periods longer than days (1)(2)(3). AD was a degenerative neurological disorder that seriously affects storage, cognitive abilities, and ability to perform the simplest basic duties.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer’ disease prediction and classification using CT images through machine learning

REDDY¹,

KRISHNA²

2023

BLL

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Numerous surveys using different techniques have been conducted in recent years to accurately classify Alzheimer's disease (AD). This research emphasized the identifi cation of AD through neuroimaging data. However, it is important to identify symptoms as soon as possible when the disease-modifying medications function best during infection before a permanent cognitive impairment develops. The use of automated algorithms to detect the early symptom of AD to this information was very important. Machine Learning (ML) has been proposed for the evaluation of various image segmentation and database techniques. In addition, Visual Geometry Group (VGG)-16 & Improved Faster Recurrent Convolutional Neural Network (IFRCNN) method developed for the ImageNet database utilizing the mathematical model based on action recognition as a feature extractor for categorization work. Experiments are being conducted on the Alzheimer's Neuroimaging Initiative (ADNI) dataset, and the proposed system achieves the 98.32 % accuracy level (Tab. 6, Fig. 4, Ref. 34).

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Section: Introductionmentioning

confidence: 99%

Alzheimer’ disease prediction and classification using CT images through machine learning

REDDY¹,

KRISHNA²

2023

BLL

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Analysis of Convolutional Neural Networks With and Without Attention Mechanisms for Pneumonia and COVID-19 Disease Prediction

Mohd,

Singh,

Shukla

et al. 2024

Applications of Parallel Data Processing for Biomedical Imaging

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Pandemic has led dramatically loss of human life and a challenge in livelihoods. An international pandemic has been caused by the contagious viral illness COVID-19. A considerable number of COVID-19 deaths are brought on by pneumonia, which is a frequent consequence of the illness. In COVID-19 patients, early diagnosis of pneumonia can enhance clinical outcomes and save lives. In this study, we suggest a modified Convolutional Neural Network-CNN model to predict patients with pneumonia and COVID-19. Dropout regularization, additional convolutional layers, and transfer learning improve the suggested model's performance. In this study attempts to develop a model that has been trained and evaluated using a publicly available dataset of chest X-ray images from COVID-19 patients, pneumonia patients, and healthy individuals.The results demonstrate that the modified CNN model outperforms the original CNN model which gives clear insight for better results on image identification.

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Medical Image Classification for Disease Prediction with the aid of Deep Learning approaches

Cited by 2 publications

References 19 publications

Alzheimer’ disease prediction and classification using CT images through machine learning

Alzheimer’ disease prediction and classification using CT images through machine learning

Analysis of Convolutional Neural Networks With and Without Attention Mechanisms for Pneumonia and COVID-19 Disease Prediction

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