Optimized Model for Predicting Gestational Diabetes using ML Techniques

Shankar, R. Shiva; Raju, Vinay; Murthy, K. V. S. S. R.; Ravibabu, D.

doi:10.1109/iceca52323.2021.9676075

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…Also, in [20], they employed D-CNN for image categorization and achieved an 84.76% success rate. CNN with GPU analyzed clinical images in [22]. In [31], the authors employed D-CNN on an imbalanced dataset and obtained 89.97% while customizing the loss function.…”

Section: Resultsmentioning

confidence: 99%

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Evaluation of deep learning models for melanoma image classification

Reddy¹,

Raju²,

Swaroop³

et al. 2023

IJPHS

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Melanin-producing cells are the origin of melanoma, the worst form of skin cancer (Melanocytes). If this cancer is not caught early, it might spread to other organs. With automated diagnostic technologies, clinicians and non- professionals may better diagnose diseases. Dermoscopic analysis, biopsy, and histological tests may be needed starting with a clinical assessment. Photo-based skin lesion categorization is challenging due to the fine-grained variability of skin lesions. We provide a more reliable melanoma detection model for each suspicious lesion in this paper. A set of characteristics characterizing a skin lesion's borders, texture, and coloursis used to educate convolutional neural networks. The deep learning models were generated using a standard dataset. To know the model's performance, consider the metrics like accuracy, sensitivity, specificity, Jaccard index and Dice coefficient. Transfer learning is used to categorize normal and diseased skin pictures automatically. This model-driven design helps doctors swiftly assess lesions.

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Section: Resultsmentioning

confidence: 99%

“…The proposed method preprocesses them to eliminate illumination and noise artefacts from clinical images. Shankar et al [22] have done their work on gestational diabetes. Attia et al [23] employed hybrid convolutional-recurrent neural networks and proposed a technique to test on 375 photographs after training on 900 images.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of deep learning models for melanoma image classification

Reddy¹,

Raju²,

Swaroop³

et al. 2023

IJPHS

View full text Add to dashboard Cite

show abstract

“…While some generic telemedicine systems have been proposed in [24], they lack detailed research and do not incorporate the use of sensory devices. In contrast, in [25], a diabetes dataset from the Kaggle Machine Learning repository was utilised. However, this dataset only includes a limited number of attributes, such as pregnancies, skin thickness, excessive thirst, blood pressure, glucose, smoking, insulin, body mass index, age, and diabetes pedigree function.…”

Section: B Remote Monitoring and Explainable Ai For Gestational Diabe...mentioning

confidence: 99%