VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images

Majib, Mohammad Shahjahan; Rahman, Md. Mahbubur; Sazzad, T. M. Shahriar; Khan, Nafiz Imtiaz; Dey, Samrat Kumar

doi:10.1109/access.2021.3105874

Cited by 132 publications

(44 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Majib et al proposed a novel idea that uses a visual geometry group (VGG) with a stack classifier. The effectiveness of the proposed method was tested on a privately collected dataset, achieving remarkable efficiency [49].The proposed method should be tested on one of the publicly available datasets. Amin et al [50] proposed a statistical learning method that was tested on two publicly available datasets, BraTS 2013 and BraTS 2015, to demonstrate the strength of the proposed method.…”

Section: Performance Evaluation Of Ensemble Methodsmentioning

confidence: 99%

A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis

Naeem

Anees

Naqvi

et al. 2022

JPM

View full text Add to dashboard Cite

Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis.

show abstract

Section: Performance Evaluation Of Ensemble Methodsmentioning

confidence: 99%

A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis

Naeem

Anees

Naqvi

et al. 2022

JPM

View full text Add to dashboard Cite

show abstract

“…Similarly, Majib ett al. [28] proposed a rethinking atrous convolution model for semantic images. Differently from the above models, rethinking atrous convolution model targets long range contexts, as it does not require convolution layers.…”

Section: A Semi-supervised Learningmentioning

confidence: 99%

“…GAN s are commonly used in the area of computer vision [42], [43], but the main problem is with training instability [28]. Many loss functions have been developed toward providing a stable training of GAN s [35].…”

Section: E(z)mentioning

confidence: 99%

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Networks

2022

View full text Add to dashboard Cite

Image segmentation is a challenging problem in medical applications. Medical imaging has become an integral part of machine learning research, as it enables inspecting interior human body with no surgical intervention. Much research has been conducted to study brain segmentation. However, prior studies usually employ one-stage models to segment brain tissues, which could lead to a significant information loss. In this paper, we propose a multi-stage Generative Adversarial Network (GAN ) model to resolve existing issues of one-stage models. To do this, we apply a coarse-to-fine method to improve brain segmentation using a multi-stage GAN . In the first stage, our model generates a coarse outline for both the background and brain tissues. Then, in the second stage, the model generates a refine outline for the white matter (W M ), gray matter (GM ), and cerebrospinal fluid (CSF ). We perform a fusion of the coarse and refine outlines to achieve high results. Despite using very limited data, we obtain an improved Dice Coefficient (DC) accuracy of up to 5% compared to one-stage models. We conclude that our model is more efficient and accurate in practice for brain segmentation of both infants and adults. In addition, we observe that our multi-stage model is 2.69−13.93 minutes faster than prior models. Moreover, our multi-stage model achieves higher performance with only a few-shot learning, in which only limited labeled data is available. Therefore, for medical images, our solution is applicable to a wide range of image segmentation applications for which convolution neural networks and one-stage methods have failed. This helps to advance the process of analyzing brain images, thus providing many advantages to the healthcare system, especially in critical health situations where urgent intervention is needed.

show abstract

“…Machine learning (ML) algorithms learn the hidden pattern of the data and can predict the class labels of unknown samples. Thus, ML is widely being used in the field of health informatics, robotics, building intelligent systems, and so forth.. [18][19][20][21][22] Similarly, ML is widely being used in the field of sentiment analysis for predicting public sentiment. 23,24 Nonetheless, It can be noticed that a significant number of studies have been conducted focusing on sentiment analysis due to the COVID-19 pandemic in specific as well as cross-country scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, ML is widely being used in the field of health informatics, robotics, building intelligent systems, and so forth. 18‐22 . Similarly, ML is widely being used in the field of sentiment analysis for predicting public sentiment 23,24 .…”

Section: Introductionmentioning

confidence: 99%

COVID‐19 and black fungus: Analysis of the public perceptions through machine learning

Islam¹,

Khan²,

Mahmud³

2021

Engineering Reports

View full text Add to dashboard Cite

While COVID‐19 is ravaging the lives of millions of people across the globe, a second pandemic “black fungus” has surfaced robbing people of their lives especially people who are recovering from coronavirus. Thus, the objective of this article is to analyze public perceptions through sentiment analysis regarding black fungus during the COVID‐19 pandemic. To attain the objective, first, a support vector machine (SVM) model, with an average AUC of 82.75%, was developed to classify user sentiments in terms of anger, fear, joy, and sad. Next, this SVM model was used to predict the class labels of the public tweets ( n = 6477) related to COVID‐19 and black fungus. As outcome, this article found public perceptions towards black fungus during COVID‐19 pandemic belong mostly to sad ( n = 2370, 36.59%), followed by joy ( n = 2095, 32.34%), fear ( n = 1914, 29.55%) and anger ( n = 98, 1.51%). This article also found that public perceptions are varied to some critical concerns like education, lockdown, hospital, oxygen, quarantine, and vaccine. For example, people mostly exhibited fear in social media about education, hospital, vaccine while some people expressed joy about education, hospital, vaccine, and oxygen. Again, it was found that mass people have an ignorance tendency to lockdown, COVID‐19 restrictions, and prescribed hygiene rules although the coronavirus and black fungus infection rates broke the previous infection records.

show abstract

VGG-SCNet: A VGG Net-Based Deep Learning Framework for Brain Tumor Detection on MRI Images

Cited by 132 publications

References 24 publications

A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis

A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis

Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Networks

COVID‐19 and black fungus: Analysis of the public perceptions through machine learning

Contact Info

Product

Resources

About