An effective model for the iris regional characteristics and classification using deep learning alex network

Thiyaneswaran, B.; Sengottaiyan, Kumarganesh; Kulandairaj, Martin Sagayam; Dang, Hien

doi:10.1049/ipr2.12630

Cited by 24 publications

(4 citation statements)

References 36 publications

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“…They had a classification accuracy of 96.6%. Thiyaneswaran et al 19 calculated that AlexNet with an ADAM solver achieved a system accuracy of 98.21%. Kumarganesh et al 20 proposed an ANFIS classifier method to classify tumors from foundation pictures.…”

Section: Literature Surveymentioning

confidence: 99%

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Prakash,

Kannan,

Santhiyakumari

et al. 2023

Sci Rep

Self Cite

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The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images. The HCNN classification technique consists of the Ridgelet transform, feature computations, classifier module, and segmentation algorithm. Pixel stability during the decomposition process was improved by the Ridgelet transform, and the features were computed from the coefficient of the Ridgelet. These features were classified using the HCNN classification approach, and tumor pixels were detected using the segmentation algorithm. The experimental results were analyzed for meningioma tumor images by applying the proposed method to the BRATS 2019 and Nanfang dataset. The proposed HCNN-based meningioma detection system achieved 99.31% sensitivity, 99.37% specificity, and 99.24% segmentation accuracy for the BRATS 2019 dataset. The proposed HCNN technique achieved99.35% sensitivity, 99.22% specificity, and 99.04% segmentation accuracy on brain Magnetic Resonance Imaging (MRI) in the Nanfang dataset. The proposed system obtains 99.81% classification accuracy, 99.2% sensitivity, 99.7% specificity and 99.8% segmentation accuracy on BRATS 2022 dataset. The experimental results of the proposed HCNN algorithm were compared with those of the state-of-the-art meningioma detection algorithms in this study.

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Section: Literature Surveymentioning

confidence: 99%

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Prakash,

Kannan,

Santhiyakumari

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Comparison experiments on a dataset of 2940 images of children with autism from Kaggle show that their model has higher accuracy. Further, Balashanmugam, et al [36] applied AlexNet to classify iris images of the eye, achieving a high correct recognition rate on 163,432 iris images, providing valuable insights into the development of biometrics. Kayadibi, et al [37] applied pre-trained AlexNet to eye condition detection, and their experimental results outperformed the traditional pre-trained deep graph convolutional network.…”

Section: Applicationsmentioning

confidence: 99%

Review of AlexNet for Medical Image Classification

Tang,

Sun,

Wang

et al. 2023

EAI Endorsed Trans e-Learn

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In recent years, the rapid development of deep learning has led to a wide range of applications in the field of medical image classification. The variants of neural network models with ever-increasing performance share some commonalities: to try to mitigate overfitting, improve generalization, avoid gradient vanishing and exploding, etc. AlexNet first utilizes the dropout technique to mitigate overfitting and the ReLU activation function to avoid gradient vanishing. Therefore, we focus our discussion on AlexNet, which has contributed greatly to the development of CNNs in 2012. After reviewing over 40 papers, including journal papers and conference papers, we give a narrative on the technical details, advantages, and application areas of AlexNet.

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“…Te methods used for calculating between-class distance include the minimum distance method, maximum distance method, middle distance method, and average linkage method. Te average linkage method utilizes the information between all samples [20,21]. Te calculation formula for between-class distance is as follows:…”

Section: Improved Clusteringmentioning

confidence: 99%

Research and Deduction of Car-to-TW Vehicle AEB Test Scenarios Based on Improved Clustering Methods

Dong,

Zhang,

Zhang

et al. 2023

Journal of Advanced Transportation

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Two-wheeled (TW) vehicle accidents are one of the major types of urban traffic accidents. TW cyclists who lack safety protection usually suffer more serious injuries and deaths in collisions. Developments in automotive active safety technologies are expected to reduce cyclist injuries and deaths, such as automatic emergency braking (AEB). To facilitate the development and testing of AEB technology, typical TW vehicle scenarios need to be constructed. Based on 400 cases of car-to-TW vehicle accident data from the National Automobile Accident In-Depth Investigation System (NAIS) database, we investigated the scenario elements that influence AEB robustness, such as weather, accident time, and road wetness. We obtained seven static scenarios using an improved clustering method, and we obtained specific speed and distance combinations in each scenario using a deduction method. Further, we compared the present findings to those of other scholars and the China New Car Assessment Program (C-NCAP). The kinematic states of the two were similar to that of C-NCAP, but the speed distribution was significantly different. The TW vehicle speed in the C-NCAP is set to 15 km/h or 20 km/h concerning the European test scenarios, but the TW vehicle speed in the present study was 10–60 km/h. Thus, the present findings recommended that subsequent C-NCAP test scenarios increase the category of motorcycles and the speed range of cars covering 20–70 km/h and consider the test conditions of bad weather and wet roads, to test the robustness of AEB.

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An effective model for the iris regional characteristics and classification using deep learning alex network

Cited by 24 publications

References 36 publications

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Review of AlexNet for Medical Image Classification

Research and Deduction of Car-to-TW Vehicle AEB Test Scenarios Based on Improved Clustering Methods

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