A Strategic Weight Refinement Maneuver for Convolutional Neural Networks

Sharma, Patrick Allan; Sharma, Amit; Kumar, Dinesh; Sharma, Anuraganand

doi:10.1109/ijcnn52387.2021.9533359

Cited by 3 publications

(7 citation statements)

References 8 publications

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“…In sum, CNNs have undeniably marked their presence across diverse applications ranging from computer vision to medical image analysis [20]. Their promise in disease prediction, most notably diabetes, is evident.…”

Section: Related Study Overview: Cnns In Disease Predictionmentioning

confidence: 99%

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Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman

Al Sadi,

Balachandran

2023

Bioengineering

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The surge of diabetes poses a significant global health challenge, particularly in Oman and the Middle East. Early detection of diabetes is crucial for proactive intervention and improved patient outcomes. This research leverages the power of machine learning, specifically Convolutional Neural Networks (CNNs), to develop an innovative 4D CNN model dedicated to early diabetes prediction. A region-specific dataset from Oman is utilized to enhance health outcomes for individuals at risk of developing diabetes. The proposed model showcases remarkable accuracy, achieving an average accuracy of 98.49% to 99.17% across various epochs. Additionally, it demonstrates excellent F1 scores, recall, and sensitivity, highlighting its ability to identify true positive cases. The findings contribute to the ongoing effort to combat diabetes and pave the way for future research in using deep learning for early disease detection and proactive healthcare.

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Section: Related Study Overview: Cnns In Disease Predictionmentioning

confidence: 99%

“…Model Training and Testing: The dataset was split into training, validation, and testing sets using the Holdout method. The CNN model was trained using different numbers of epochs (10,20,30,50,100,150,200). For each epoch value, the model was trained and tested, and the performance metrics were evaluated.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman

Al Sadi,

Balachandran

2023

Bioengineering

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“…CNN has been considered by researchers as the most performant tool because of its great learning capacity due to the utilization of many feature extraction stages that can learn representations from data automatically and with a higher percentage of accuracy (Sharma, 2021). Other image classification processes, CNN could be applied are Search Engines, Social-Media, and Recommender Systems.…”

Section: Medical Image Analysis and Classification Processmentioning

confidence: 99%

“…This includes social media, identification, and surveillance. Such images could include human faces or other living beings such as animals, fish, and insects (Sharma, 2021).…”

Section: Face Recognition Rnn Applicationsmentioning

confidence: 99%

“…Drug discovery using predictive analytics is another major healthcare field that makes extensive use of CNNs and is one of the most recognized inventive uses of convolutional neural networks (Sharma, 2021). According to Vamathevan et al (2019), drug discovery and development pipelines are long, and complex and depend on numerous factors.…”

Section: Drug Discovery Using Predictive Analyticsmentioning

confidence: 99%

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Convolutional Neural Network (CNN): The architecture and applications

AYENI

2022

Appl. J. Phys. Sci.

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The human brain is made up of several hundreds of billions of interconnected neurons that process information in parallel. Researchers in the field of artificial intelligence have successfully demonstrated a considerable level of intelligence on chips and this has been termed Neural Networks (NNs). Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning (ML) and they are at the heart of deep learning algorithms. These subsets of ML have their names and structures derived from the human brain and the way that biological neurons signal to one another. A class of NNs that are often used in processing digital data images is the Convolutional Neural Network (CNN or ConvNet). The human brain processes a huge amount of information with each neuron having its own receptive field connected to other neurons in a way that they cover the entire visual field. Mimicking the biological technique, where the neurons only respond to stimuli in the restricted region of the visual field referred to as the receptive field, each neuron in the CNN processes data only in its receptive field. In this review paper, the architecture and application of CNN are presented. Its evolution, concepts, and approaches to solving problems related to digital images, computer vision and are also examined.

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A Guided Neural Network Approach to Predict Early Readmission of Diabetic Patients

Ram,

Ali,

Krishna

et al. 2023

IEEE Access

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Diabetes is a major chronic health problem affecting millions globally. Effective diabetes management can reduce the risk of hospital readmission and the associated financial losses for both the healthcare system and insurance companies. Hospital readmission is a high-priority healthcare quality measure that reflects the inadequacies in the healthcare system that also increase healthcare costs and negatively influence hospitals' reputation. Predicting readmissions in the early stages prompts great attention to patients with a high risk of readmission. There has been some attempt in applying machine learning predictive models such as ensemble learning with Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) to correctly identify if the readmission can happen within 30 days (<30 days) or it may never happen or happens after 30 days (≥30 days). We are proposing a new method that is applied to ANN to guide it through its gradient descent optimizers by realizing consistent vs inconsistent data in every batch. Our results show that there are up to 1.5% improvement in classification accuracies in both 2-class and 3-class variations of the experimented benchmark dataset when using the guided optimizer to train the ANN as opposed to the standard optimizer. Guided ANN is also able to achieve better error convergence than standard ANN.

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A Strategic Weight Refinement Maneuver for Convolutional Neural Networks

Cited by 3 publications

References 8 publications

Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman

Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman

Convolutional Neural Network (CNN): The architecture and applications

A Guided Neural Network Approach to Predict Early Readmission of Diabetic Patients

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