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hybridized densenet201 and mobilenet with svm classifier  (Keerthana Industries)

 
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    Keerthana Industries hybridized densenet201 and mobilenet with svm classifier
    Hybridized Densenet201 And Mobilenet With Svm Classifier, supplied by Keerthana Industries, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svm+classifier/hybrid+cnn+models/pmc12092923-16-22-0
    Average 90 stars, based on 1 article reviews
    hybridized densenet201 and mobilenet with svm classifier - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    other:

    Article Title: An explainable and federated deep learning framework for skin cancer diagnosis
    Article Snippet: Keerthana et al [ ]/2023 , ISBI2016 , A combined network (DenseNet201 + MobileNet + SVM) that obtained best accuracy of 88.02%. , Privacy-preserving is needed that makes the model more reliable and dependable..

    Article Title: Decoding skin cancer classification: perspectives, insights, and advances through researchers' lens.
    Article Snippet: Figure 10 depicts the overview of this model. Keerthana et al.186 utilized a hybrid CNN architecture of DenseNet201 and MobileNet to capture both lowlevel features like textures and edges and high-level features like lesion patterns and shapes.

    Article Title: An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models.
    Article Snippet: Keerthana et al.12 introduce two innovative hybrid CNN methods at an output layer for categorizing images.

    Article Title: An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models
    Article Snippet: Keerthana et al. introduce two innovative hybrid CNN methods at an output layer for categorizing images.

    Article Title: Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach
    Article Snippet: Keerthana et al. [ 14 ] , - , Hybrid CNN-SVM , Highest accuracy reported was 88.02% for the hybridized DenseNet201 and Mobilenet with SVM classifier , The reported outcome was not satisfactory for the practical application.

    Article Title: MobileYOLO-Cyano: An enhanced deep learning approach for precise classification of cyanobacterial genera in water quality monitoring.
    Article Snippet: Cyanobacteria pose a critical challenge for freshwater management due to their ability to rapidly proliferate and produce toxins that can jeopardize human health, even at low concentrations.. Therefore, effective methods to accurately classify cyanobacterial genera are essential for water quality assessment.. However, existing automated classification methods often suffer from low accuracy or limitations in identifying cyanobacterial genera.

    Article Title: OPTUNA optimization for predicting chemical respiratory toxicity using ML models.
    Article Snippet: Predicting molecular toxicity is an important stage in the process of drug discovery.. It is directly related to medical destiny and human health.. This paper presents an enhanced model for chemical respiratory toxicity prediction.

    Article Title: Decoding skin cancer classification: perspectives, insights, and advances through researchers’ lens
    Article Snippet: Figure depicts the overview of this model. Keerthana et al. utilized a hybrid CNN architecture of DenseNet201 and MobileNet to capture both low-level features like textures and edges and high-level features like lesion patterns and shapes.



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    Image Search Results


    Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

    Journal: CNS Neuroscience & Therapeutics

    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Comparative biomarker performance in SVM classification. SVM models leveraging dynamic functional connectivity (dFC) demonstrated superior classification performance (C, F), outperforming models based on regional indices PerAF (A, D) and dALFF (B, E). For each biomarker, the grid‐search optimized parameters (top) and validation ROC curves (bottom) are shown, underscoring dFC as a highly discriminative feature for identifying disease‐specific neural signatures.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Biomarker Discovery, Functional Assay

    Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Journal: CNS Neuroscience & Therapeutics

    Article Title: Decoding Post‐Stroke Cognitive Impairment After Acute Basal Ganglia Infarction: The Synergistic Role of Functional Segregation and Integration in an SVM fMRI Framework

    doi: 10.1002/cns.70871

    Figure Lengend Snippet: Enhanced diagnostic classification using combined biomarkers. Integration of multimodal neuroimaging metrics (PerAF, dALFF, dFC) yields a powerful classifier for PSCI. The SVM model, optimized via grid search (A), achieves superior discriminatory performance, as evidenced by the ROC curve in (B), outperforming models based on single metrics.

    Article Snippet: PSCI patients exhibit altered cerebellar‐cortical dynamics in PerAF, dALFF, and dFC, and an SVM classifier based on dFC features achieves 94.52% accuracy and 0.98 AUC, outperforming single‐metric models.

    Techniques: Diagnostic Assay