A Framework for Early Recognition of Alzheimer’s Using Machine Learning Approaches

Kumar, Lolla Kiran; Rao, P. Srinivasa; Rao, S. Sreenivasa

doi:10.1007/978-981-19-4863-3_1

Cited by 2 publications

(2 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These characteristics most likely included 80% accuracy-achieved kernel size, shape, color, and texture. The Backpropagation Neural Network (BPNN) outperformed the array of machine vision classification algorithms developed, attaining an accuracy of 85% [19]. In [20], an innovative method utilizing the "shadow to total-area ratio" effectively addressed the classification difficulty of differentiating between whole and split-down cashews, obtaining an impressive degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based System for of Grading Cashew Kernels

2024

IJSREM

View full text Add to dashboard Cite

Cashew nuts are cultivated widely across the globe and are categorized into various classes according to their size and quality. Currently, cashew kernels are sorted and graded manually, which is time-consuming and labor-intensive. This research proposes hybrid models to automate and accelerate the cashew classification process by integrating classifiers with deep convolutional neural networks (DCNNs). The proposed hybrid models categorize cashew kernels into five groups: W180, W210, W300, W400, and W500. Various hybrid models, including RCNN + OpenCV, were implemented and evaluated based on specificity, accuracy, and other metrics. The results demonstrated that the RCNN model paired with OpenCV achieved a max- imum accuracy of 90%. This study’s findings highlight that automating cashew grading can be significantly enhanced by combining DCNNs with classifiers in hybrid models. These hybrid models improve efficiency by enabling automatic, effective, and precise classification of cashews. By leveraging advanced machine learning techniques, the process becomes faster, more accurate, and less dependent on manual labor. Overall, the integration of deep learning technologies with traditional classifiers offers a promising approach to modernizing and optimizing the cashew grading industry. Future research could further refine these hybrid models to achieve even higher accuracy and efficiency, potentially revolutionizing agricultural sorting processes beyond cashew nuts. Index Terms—DCNN, OpenCV, RCNN, Hybrid Models

show abstract

Section: Introductionmentioning

confidence: 99%

Deep Learning Based System for of Grading Cashew Kernels

2024

IJSREM

View full text Add to dashboard Cite

show abstract

“…The deployment of computer-based AD diagnosis can assist clinicians in identifying high-risk groups, thereby preventing disease progression and enabling early diagnosis [11]. Magnetic Resonance Imaging (MRI), a common medical imaging method, offers detailed visualization of brain structures for AD detection [12].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Deep Learning for Enhanced Detection of Alzheimer’s Disease Through Morphometric Analysis of Brain Images

Çelebi,

Emiroğlu

2023

View full text Add to dashboard Cite

This study investigates the efficacy of tensor-based morphometry (TBM) in detecting Alzheimer's Disease (AD) using deep learning techniques. The primary focus is on discerning the volumetric variations in brain tissues characteristic of AD, Mild Cognitive Impairment (MCI), and cognitively normal (CN) conditions. TBM, as a measure of minute local volume differences, is employed as the distinguishing feature. The results are juxtaposed with those obtained from machine-learning-based methods, trained using a variety of medical images. Three unique models were developed for this purpose. The first model, trained using medial slices of the brain (train: 1622; test: 406), displayed an accuracy of less than 50%. The second model utilized axial brain slices procured at 5-pixel intervals, encompassing the hippocampus and the temporal lobe (train: 1632; test: 406), and demonstrated a significantly improved accuracy of 93%. The third model, fine-tuned with small kernel sizes to better extract localized changes from the image data used in the second model, achieved an accuracy of 92%. The findings suggest that the application of TBM and deep learning to medial slices alone is insufficient for an accurate diagnosis of AD. However, employing TBM with deep learning techniques to slices covering the hippocampus and temporal lobe can potentially offer a highly accurate approach for early AD detection. Notably, the use of small filters to extract detailed features from TBM did not enhance the model's performance. This research underscores the potential of deep learning in advancing the field of AD detection and diagnosis, providing crucial insights into the future development of diagnostic tools.

show abstract

A Framework for Early Recognition of Alzheimer’s Using Machine Learning Approaches

Cited by 2 publications

References 35 publications

Deep Learning Based System for of Grading Cashew Kernels

Deep Learning Based System for of Grading Cashew Kernels

Leveraging Deep Learning for Enhanced Detection of Alzheimer’s Disease Through Morphometric Analysis of Brain Images

Contact Info

Product

Resources

About