VGG-UNET for Brain Tumor Segmentation and Ensemble Model for Survival Prediction

Nawaz, Ali; Akram, Usman; Salam, Anum Abdul; Ali, Amad Rizwan; Rehman, Attique Ur; Zeb, Jahan

doi:10.1109/icrai54018.2021.9651367

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…However, this method may require significant resources. Ali et al [20] propose an overall approach for tumor segmentation using VGG-Unet, a variant of the U-net model. The advantage of this approach is in reducing data dimensionality while preserving important information.…”

Section: Ensemble Methods For Feature Extractionmentioning

confidence: 99%

A new ensemble method for brain tumor segmentation

Laouali,

Chebbah,

Nakouri

2024

Multimed Tools Appl

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In the medical field, Magnetic resonance image (MRI) brain tumor segmentation is highly crucial and important. Tumor segmentation can help in diagnosis and prognosis, overall growth predictions, Tumor density measures, and care treatment plans needed for patients. However, this task is extremely challenging due to low contrast, noise in medical images, and to the voluminous size of data. This work focuses on improving medical image semantic segmentation using a time-efficient ensemble learning approach. We propose a novel ensemble deep approach using a Convolutional Neural Network and three Autoencoders to extract relevant features from brain MRIs, reduce dimensionality, then apply supervised learning for pixel-by-pixel binary classification to achieve tumor segmentation. Experiments show promising results with our model achieving a Dice Similarity Coefficient of 88.63%, a Jaccard Similarity Index of 79.58% and a Recall of 84.34%, thus demonstrating the model's ability to identify the tumor's pixels with precision while consuming fewer resources. Therefore, the segmentation performance is sufficiently addressed in terms of accuracy, reliability and speed.

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Section: Ensemble Methods For Feature Extractionmentioning

confidence: 99%

A new ensemble method for brain tumor segmentation

Laouali,

Chebbah,

Nakouri

2024

Multimed Tools Appl

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“…Earlier publications in the literature [18,19] support the UNet and its derivatives. The application of VGG-UNet in image extraction can be found in the studies [20,21]. Khan et al [9] proposed the VGG-UNet scheme that can be used to extract CC from selected test images.…”

Section: Vgg-unetmentioning

confidence: 99%

Enhanced Karyotyping Through Deep Learning-Assisted Segmentation and Classification of Chromosomal Cells

Sekar,

Sankaran

2024

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The escalating prevalence of genetic disorders in humans underscores the criticality of early detection. Current clinical methodologies, such as cellular activity assessment and chromosomal examination, are widely employed for early-stage prediction of these disorders. However, traditional approaches to chromosomal cell (CC) examination are intricate and labor-intensive. This study proposes a novel deep learning framework (DLF) to overcome these challenges by precisely and efficiently segmenting and classifying CCs. As part of this scheme, images are collected and resized, the DLF is trained using the selected images, segmentation and deep feature extraction of the CC are performed. The proposed methodology involves image collection and resizing, training the DLF with chosen images, CC segmentation and feature extraction and multiclass classification and performance verification. This work implements the VGG-UNet plan to examine the chosen CC images collected from the Biomedical Imaging Laboratory (BioImLab). An experimental inquiry is being carried out on 5474 images and the achieved findings are addressed. The findings of this research suggest that the presented work help to provide a detection accuracy of >98% with the K-Nearest Neighbor supported classifier. This research can be expanded in the future to detect the genetic disorder based on the information obtained from the CC.

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“…These images were provided by the Computational Biology and Complex Systems Group at Universitat Politecnica de Catalunya. While this study provided [43], diagnosis of epileptic seizure in EEG [44], automatically detect pneumonia in X-ray images [45], screening COVID-19 in chest X-ray images [46], detecting COVID-19 from CT scans [47], and knee osteoarthritis classification in MRI [48] Drug discovery: Target identification and drug repurposing [49], predicting constitutive androstane receptor agonists [50], predicting molecules' effects to find SARS-CoV-2 drugs [51], and Vaccine: Creating a vaccine: SARS-CoV-2 example [52] VGGNet Diagnosis: Detecting COVID-19 in chest X-ray [53], classifying the ocular disease in eye image [54], detection of pneumonia from Chest X-Ray [55], early detection of skin cancer [56], and tuberculosis detection in X-Ray Image [57] ResNet Diagnosis: Diagnose intracranial hemorrhage in CT Scanning [58], COVID-19 diagnosis from X-ray images [59], diagnosis of knee osteoarthritis [60], and automatic schizophrenia detection from EEG [61] U-Net Diagnosis: Segmenting COVID-19 chest CT images [62], brain tumor segmentation and survival prediction [63], liver and lesion segmentation [64], and dental CBCT images segmentation [65] GAN Generating structured data in the medical domain [66], low-dose CT denoising [67], data augmentation in breast ultrasound mass classification [68], and ECG denoising framework [69] valuable insights into the use of deep learning for microscopic examination, the authors suggested that the model's performance could be improved by using more images to train it and implementing a more accurate method for annotation. This highlights the importance of accurate annotation in deep learning, which is critical to the performance of the model.…”

Section: Leishmaniasis Diagnosismentioning

confidence: 99%

Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis

Sadeghi,

Fakhar

et al. 2024

Transboundary and Emerging Diseases

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Leishmania, a single-cell parasite prevalent in tropical and subtropical regions worldwide, can cause varying degrees of leishmaniasis, ranging from self-limiting skin lesions to potentially fatal visceral complications. As such, the parasite has been the subject of much interest in the scientific community. In recent years, advances in diagnostic techniques such as flow cytometry, molecular biology, proteomics, and nanodiagnosis have contributed to progress in the diagnosis of this deadly disease. Additionally, the emergence of artificial intelligence (AI), including its subbranches such as machine learning and deep learning, has revolutionized the field of medicine. The high accuracy of AI and its potential to reduce human and laboratory errors make it an especially promising tool in diagnosis and treatment. Despite the promising potential of deep learning in the medical field, there has been no review study on the applications of this technology in the context of leishmaniasis. To address this gap, we provide a scoping review of deep learning methods in the diagnosis of the disease, drug discovery, and vaccine development. In conducting a thorough search of available literature, we analyzed articles in detail that used deep learning methods for various aspects of the disease, including diagnosis, drug discovery, vaccine development, and related proteins. Each study was individually analyzed, and the methodology and results were presented. As the first and only review study on this topic, this paper serves as a quick and comprehensive resource and guide for the future research in this field.

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VGG-UNET for Brain Tumor Segmentation and Ensemble Model for Survival Prediction

Cited by 17 publications

References 22 publications

A new ensemble method for brain tumor segmentation

A new ensemble method for brain tumor segmentation

Enhanced Karyotyping Through Deep Learning-Assisted Segmentation and Classification of Chromosomal Cells

Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis

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