Design of biosensor for synchronized identification of diabetes using deep learning

Armghan, Ammar; Logeshwaran, Jaganathan; Sutharshan, S.M.; Aliqab, Khaled; Alsharari, Meshari; Patel, Shobhit K.

doi:10.1016/j.rineng.2023.101382

Results in Engineering

2023

DOI: 10.1016/j.rineng.2023.101382

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Design of biosensor for synchronized identification of diabetes using deep learning

Ammar Armghan,

Jaganathan Logeshwaran,

S.M. Sutharshan

et al.

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Cited by 79 publications

(1 citation statement)

References 42 publications

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“…With a high accuracy rate of 95.7%, this categorization approach presents a significant opportunity for accurate diabetes diagnosis from ECG data. The work in[33] introduces a deep learning-based, highly sensitive biosensor for synchronized diabetes diagnosis. The platform, which integrates nanotechnology and electrochemical techniques, offers sensitivity and selectivity in detecting glucose molecules, enabling quick and precise diabetes diagnosis in a single test.…”

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confidence: 99%

A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

Avinash Bhagat, Tawseef Ahmed Teli

2024

jes

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Deep learning is a highly useful technique for the early identification of diabetes mellitus, according to the study done by numerous authors over the past few decades. By using pre-processing techniques on the dataset to get rid of various anomalies like over-fitting, under-fitting, redundancy, missing values, and non-significant features to make it more efficient for analysis, it is possible to increase the effectiveness of deep learning algorithms for diagnosing the disease. This work addresses the global problem of diabetes by exploring a revolutionary deep-learning method for early identification. Conventional convolutional neural network (CNN) models have drawbacks when used with numerical medical datasets, like this study's PIMA Indians Diabetes Database. The article suggests a technique for transforming numerical data into visual representations depending on feature relevance to get over this obstacle. This conversion makes it possible to use strong CNN models for diabetes early diagnosis. Classifying the created diabetic images after feeding them into CNN architectures that have already been trained on VGG16 and ResNet50. The promising outcomes with an accuracy of 97.19% demonstrate the possibility of the suggested strategy for improving diabetes detection and validating the effectiveness of diabetes imaging in obtaining an early diagnosis.

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A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

Avinash Bhagat, Tawseef Ahmed Teli

2024

jes

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Exploring the potential of ionic liquid-based electrochemical biosensors for real-time biomolecule monitoring in pharmaceutical applications: From lab to life

Thakur,

Kumar

2023

Results in Engineering

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Arsenic and type 2 diabetes: Revealing the environmental exposure relationship through effective factors - A systematic review

Abolli,

Dehghani,

Atlasi

et al. 2024

Results in Engineering

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Design of biosensor for synchronized identification of diabetes using deep learning

Cited by 79 publications

References 42 publications

A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

A Deep Learning-Based Diabetes Diagnosis Model on PIMA Image Dataset

Exploring the potential of ionic liquid-based electrochemical biosensors for real-time biomolecule monitoring in pharmaceutical applications: From lab to life

Arsenic and type 2 diabetes: Revealing the environmental exposure relationship through effective factors - A systematic review

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