The diagnosis of brain tumor from MRI is a complex problem considered to be one of the most important aspects in medical diagnosis since timely detection along with correct categorization can considerably enhance patients’ prognosis. This paper aims at investigating the use of the two main deep learning architectures, namely ResNet and DenseNet, for brain tumor diagnosis. Specifically, these pre-trained models were used through transfer learning and fine-tuned on brain tumor MRI scans dataset to evaluate the performance metrics such as accuracy, sensitivity, specificity, precision, and F1-score. ResNet fixes the vanishing gradient problem that is inherent to deep learning and lets one train deeper models. DenseNet as a result of the dense connectivity between the layers; enhances feature reuse and gradient flow, it therefore makes better use of the parameters. The experimental results show that DenseNet achieved a higher validation accuracy of $95 \%$, while ResNet achieved $85 \%$ even with a seemingly more difficult distribution, with only $12 \%$ and $37 \%$, pointing to better generalization and stability of the receiving function. Model improving techniques included random rotations of images, flipping, and random changes in image color intensity. Precise learning rate management strategies such as learning rate scheduling helped to stabilize the training process, DenseNet among them. The confusion matrix analysis proceeding it further affirmed DenseNet’s performance in ‘No Tumor’ and ‘Tumor’ differentiation. In this objective comparison, DenseNet’s ability for better and accurate brain tumor detection when applied in a clinical environment has been postulated. Further works should consider incorporating tumor localization to the classification technique, investigate new network architectures further and, test such algorithms in authentic clinic setting to build a more efficient diagnostic tool to improve patients’ treatment.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning-based MRI Analysis for Brain Tumor Detection: Insights from ResNet and DenseNet Models


    Beteiligte:
    Padmakala, S. (Autor:in) / Uma Maheswari, S (Autor:in)


    Erscheinungsdatum :

    06.11.2024


    Format / Umfang :

    451145 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Advancing Oral Disease Diagnosis with Deep Learning and DenseNet Architecture

    Vijayalakshmi, M. / Tandon, Sajal / Gupta, Tanay et al. | IEEE | 2024


    GHM-DenseNet Intrusion Detection Method Based on GAN

    Ren, Jianhua / Sun, Zhenyu | IEEE | 2022


    Automatic Detection of Brain Tumor Using Deep Learning Algorithms

    Sangeetha, R. / Mohanarathinam, A. / Aravindh, G. et al. | IEEE | 2020