inception v3 (Kaggle Inc)

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Kaggle Inc inception v3
Inception V3, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/inception+v3/inception+v3/pmc12518656-15-14-24
Average 86 stars, based on 1 article reviews

inception v3 - by Bioz Stars, 2026-09

86/100 stars

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Article Title: Diabetic retinopathy detection via exudates and hemorrhages segmentation using iterative NICK thresholding, watershed, and Chi 2 feature ranking.
Article Snippet: .. Neural network architectures and logistic model trees: Xception, Inception-V3, Inception-Resnet-V2 and Logistic model trees (LMT) were employed 88,702 images from Kaggle dataset Sensitivity 99.21 Specificity 97.59 Sreng et al.32 Morphological-top-hat and Kirsch edge-detection techniques to extract 208 characteristics and employed hybrid simulated annealing to choose the best feature set for an ensemble bagging classifier 1,200 retinal images of local dataset Accuracy 97.08 Sensitivity 90.90 Specificity 98.92 Asia et al.33 CNN with distinct residual neural network (ResNet) structures, namely ResNet-101, ResNet-50, and VggNet-16 1,607 images from DiaretDB1-MA, DiaretDB1HM, e-ophtha and ROCh datasets Accuracy 95.00–100.00 GAO rt al.34 Deep convolutional neural network (DCNN) models were trained for the diagnosis of DR 4,476 images of local dataset Accuracy 88.72 Gulshan et al.35 Used a deep convolutional neural network that was trained on a retrospective dataset of 128,175 retinal images, graded multiple times, to identify diabetic retinopathy cases 11,711 images from EyePACS-1 and Messidor-2 datasets Specificity 98.30 Ting et al.36 Deep Learning System (DLS) -Convolution neural network (CNN) for DR pattern learning 112, 648 images of local dataset Sensitivity 90.50 Specificity 91.60 Rajalakshmi et al.37 Deep Learning-based AI EyeArt was used for DR screening and grading. .. It was applied to smartphonecaptured images (Remidio) 296 images of local dataset Sensitivity 95.80 Specificity 80.20 Sanya et al.38 CLAHE and morphological image processing technique 463 images of E-optha Accuracy 99.70 Rahman et al.39 Support vector machine (SVM) and deep neural network (DNN) 560 images from Kaggle, DDR, Zenodo, and Mendeley AUC 99.15 Kumar et al.40 Two-field mydriatic fundus photography was used to identify DR and Non-DR 1,344 images of the local dataset Sensitivity 80 00 Butt et al.41 A hybrid technique for detecting and classifying DR with Transfer Learning-based Hybrid GoogleNet and ResNet-18 features with SVM Classifier 3,662 images of APTOS dataset Accuracy 97.80 Revathy et al.42 A hybrid classifier: support vector machine, k nearest neighbour, random forest, logistic regression, multilayer perceptron network 244 images from Kaggle Accuracy 82.00 Naramala et al.43 Restricted Boltzmann Machines (RBM), automate diagnostic processes in retinal images using a U-network model for optic segmentation and the squirrel search algorithm for optimal performance 485 images from RIM-ONE DL Accuracy 99.20 Lu1 L et al.44 A convolutional neural network to classify fundus images.

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Article Title: A Survey on Deep Learning Techniques for Predictive Analytics in Healthcare
Article Snippet: Healthcare data is growing at more than 50% annually, making it one of the most rapidly expanding data in the digital world.. Clinical problem-solving is a difficult skill that doctors must have in order to provide excellent care.. This skill’s accuracy is critical to the patients’ lives and well-being.

Article Title: Automated Diabetic Retinopathy Detection Using Convolutional Neural Networks For Feature Extraction And Classification (ADRFEC)
Article Snippet: Diabetic Retinopathy (DR) is a significant complication of Diabetes Mellitus, leading to various retinal abnormalities that can impair vision and, in severe cases, result in blindness.. Approximately 80% of patients with long-standing diabetes for 10–15 years develop DR.. The manual process of diagnosing and detecting DR for timely treatment is both time-consuming and unreliable, mainly due to resource constraints and the need for expert opinion.

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