A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNET

Pani, Kaliprasad; Chawla, Indu

doi:10.1016/j.compeleceng.2024.109418

Computers and Electrical Engineering

2024

DOI: 10.1016/j.compeleceng.2024.109418

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A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNET

Kaliprasad Pani,

Indu Chawla

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Cited by 3 publications

References 31 publications

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An explainable Liquid Neural Network combined with path aggregation residual network for an accurate brain tumor diagnosis

Shaheema,

K.,

Muppalaneni

2025

Computers and Electrical Engineering

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An explainable Liquid Neural Network combined with path aggregation residual network for an accurate brain tumor diagnosis

Shaheema,

K.,

Muppalaneni

2025

Computers and Electrical Engineering

View full text Add to dashboard Cite

Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images

Alshardan,

Alruwais,

Alqahtani

et al. 2024

Sci Rep

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Recognition and segmentation of brain tumours (BT) using MR images are valuable and tedious processes in the healthcare industry. Earlier diagnosis and localization of BT provide timely options to select effective treatment plans for the doctors and can save lives. BT segmentation from Magnetic Resonance Images (MRI) is considered a big challenge owing to the difficulty of BT tissues, and segmenting them from the healthier tissue is challenging when manual segmentation is done through radiologists. Among the recent proposals for the brain segmentation method, the BT segmentation method based on machine learning (ML) and image processing could be better. Thus, the DL-based brain segmentation method is extensively applied, and the convolutional network has better brain segmentation effects. The deep convolutional network model has the problem of a large loss of information and a large number of parameters in the encoding and decoding processes. With this motivation, this article presents a new Deep Transfer Learning with Semantic Segmentation based Medical Image Analysis (DTLSS-MIA) technique on MRI images. The DTLSS-MIA technique aims to segment the affected BT area in the MRI images. At first, the presented method utilizes a Median filtering (MF) approach to optimize the quality of MRI images and remove the noise. For the semantic segmentation method, the DTLSS-MIA method follows DeepLabv3 + with a backbone of the EfficientNet model for determining the affected brain region. Moreover, the CapsNet architecture is employed for the feature extraction process. Lastly, the crayfish optimization (CFO) technique with diffusion variational autoencoder (D-VAE) architecture is used as a classification mechanism, and the CFO technique effectively tunes the D-VAE hyperparameter. The simulation analysis of the DTLSS-MIA technique is validated on a benchmark dataset. The performance validation of the DTLSS-MIA technique exhibited a superior accuracy value of 99.53% over other methods.

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Transfer Learning Approaches for Brain Metastases Screenings

Luu,

Tuchinov,

Suvorov

et al. 2024

Biomedicines

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Background: In this study, we examined the effectiveness of transfer learning in improving automatic segmentation of brain metastases on magnetic resonance imaging scans, with potential applications in preventive exams and remote diagnostics. Methods: We trained three deep learning models on a public dataset from the ASNR-MICCAI Brain Metastasis Challenge 2024, fine-tuned them on a small private dataset, and compared their performance to models trained from scratch. Results: Results showed that models using transfer learning performed better than scratch-trained models, though the improvement was not statistically substantial. The custom Tversky and Binary Cross-Entropy loss function helped manage class imbalance and reduce false negatives, limiting missed tumor regions. Medical experts noted that, while fine-tuned models worked well with larger, well-defined tumors, they struggled with tiny, scattered tumors in complex cases. Conclusions: This study highlights the potential of transfer learning and tailored loss functions in medical imaging, while also pointing out the models’ limitations in detecting very small tumors in challenging cases.

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A hybrid approach for multi modal brain tumor segmentation using two phase transfer learning, SSL and a hybrid 3DUNET

Cited by 3 publications

References 31 publications

An explainable Liquid Neural Network combined with path aggregation residual network for an accurate brain tumor diagnosis

An explainable Liquid Neural Network combined with path aggregation residual network for an accurate brain tumor diagnosis

Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images

Transfer Learning Approaches for Brain Metastases Screenings

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