Brain Tumor Segmentation using Multimodal MRI and Convolutional Neural Network

Loghmani, Nazila; Moqadam, Roqaie; Allahverdy, Armin

doi:10.1109/icee55646.2022.9827274

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…In specific applications, four separate binary classifiers based on ResNet101 have been trained to discriminate cystoid macular edema, macular hole, epiretinal membrane, and serous macular detachment from OCT images [9]. It is noteworthy that, in the training of these convolutional neural networks, pre-trained weights derived from the ImageNet dataset have commonly served as initial weights or feature extractors [24,26,31]. These weights, obtained through the extensive training of image datasets, offer a valuable starting point for OCT image classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based retinal abnormality detection from OCT images with limited data

Mohammad Talebzadeh,

Abolfazl Sodagartojgi,

Zahra Moslemi

et al. 2024

World J. Adv. Res. Rev.

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In the realm of medical diagnosis, the challenge posed by retinal diseases is considerable, given their potential to complicate vision and overall ocular health. A promising avenue for achieving highly accurate classifiers in detecting retinal diseases involves the application of deep learning models. However, overfitting issues often undermine the performance of these models due to the scarcity of image samples in retinal disease datasets. To address this challenge, a novel deep triplet network is proposed as a metric learning approach for detecting retinal diseases using Optical Coherence Tomography (OCT) images. Incorporating a conditional loss function tailored to the constraints of limited data samples, this deep triplet network enhances the model’s accuracy. Drawing inspiration from pre-trained models such as VGG16, the foundational architecture of our model is established. Experiments use open-access datasets comprising retinal OCT images to validate our proposed approach. The performance of the suggested model is demonstrated to surpass that of state-of-the-art models in terms of accuracy. This substantiates the effectiveness of the deep triplet network in addressing overfitting issues associated with limited data samples in retinal disease datasets.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based retinal abnormality detection from OCT images with limited data

Mohammad Talebzadeh,

Abolfazl Sodagartojgi,

Zahra Moslemi

et al. 2024

World J. Adv. Res. Rev.

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The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Amiri,

Heidari,

Navimipour

et al. 2024

Neural Comput & Applic

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Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability.

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Enhancing office building energy efficiency: neural network-based prediction of energy consumption

Momeni,

Eghbalian,

Talebzadeh

et al. 2024

J Build Rehabil

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Brain Tumor Segmentation using Multimodal MRI and Convolutional Neural Network

Cited by 3 publications

References 13 publications

Deep learning-based retinal abnormality detection from OCT images with limited data

Deep learning-based retinal abnormality detection from OCT images with limited data

The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Enhancing office building energy efficiency: neural network-based prediction of energy consumption

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