An Overview of Segmentation and Classification Techniques: A Survey of Brain Tumour-Related Research

Awang, Muhammad Bakhtiar Iman; Ibrahim, Shafaf

doi:10.1109/aidas53897.2021.9574170

Cited by 3 publications

(4 citation statements)

References 56 publications

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“…Recent advances in Deep Learning-based methods have refined occluded object detection [22,25,27]. As for in-depth processes and subprocess in medical related image processing techniques, paper review by Awang and Ibrahim [28] has it covered.…”

Section: Occluded Object Detection In Medical Imagingmentioning

confidence: 99%

“…Several scenarios were tested in that regard, resulting with it only being able to handle two or three particles. It is good to note that the developed method was intended and applied to track these particles, meaning that it was not applied on still images to only detect occluded particles but rather real-time tracking of occluded particles [28]. With the Global Hungarian algorithm, further application and testing on real-time MRI, CT and ultrasound scans could prove beneficial on automated diagnosis studies.…”

Section: Occluded Object Detection In Medical Imagingmentioning

confidence: 99%

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A Review on Occluded Object Detection and Deep Learning-Based Approach in Medical Imaging-Related Research

Khairul Rohaizzat Jamaluddin,

Shafaf Ibrahim

2023

ARASET

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Medical image occlusions can arise due to several factors, including anatomical features, imaging modality, and acquisition settings. Object occlusion occurrence has a very significant effect on detection accuracy in general and in medical cases, these effects hinder proper diagnosis and treatment plans that may be fatal for the patients. Thus, precise occluded object detection is imperative. This paper aims to review the various state-of-the-art models and approaches that had been proposed for the occluded object detection. The coverage of this paper includes occluded object detection models in other applications and medical imaging, its proposed Deep Learning implementations, hybrid Deep Learning, and statistical analysis that were integrated into Deep Learning models. It is found that, in overall, more annotated medical image datasets are required to reduce overfitting occurrence, numerous Deep Learning models and its hybrid combination’s applications yet to be tested of its limitations, and the extent of statistical analysis integration on Deep Learning models.

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Section: Occluded Object Detection In Medical Imagingmentioning

confidence: 99%

Section: Occluded Object Detection In Medical Imagingmentioning

confidence: 99%

A Review on Occluded Object Detection and Deep Learning-Based Approach in Medical Imaging-Related Research

Khairul Rohaizzat Jamaluddin,

Shafaf Ibrahim

2023

ARASET

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“…1 The use of deep learning, a type of AI, has been shown to be effective in the detection of cytopathic abnormalities. 2 The domains of image analysis and computer vision are particularly well-suited for the application of deep learning to real-world situations. 3 When it comes to image classification and detection issues, the convolutional neural network (CNN) stands out as a top deep learning model, 4 due to its ability to automatically extract features from images and accurately classify or segment them.…”

Section: Introductionmentioning

confidence: 99%

“…AI can help medical professionals make more accurate diagnoses, personalise treatment plans and detect diseases earlier, leading to better patient outcomes 1 . The use of deep learning, a type of AI, has been shown to be effective in the detection of cytopathic abnormalities 2 . The domains of image analysis and computer vision are particularly well‐suited for the application of deep learning to real‐world situations 3 .…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of state‐of‐the‐art CNN architectures for brain tumour detection

Khushi,

Masood,

Jaffar

et al. 2023

Int J Imaging Syst Tech

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Deep learning models, such as convolutional neural network (CNN), are popular now a day to solve various complex problems in medical and other fields, such as image classification, object detection, recommendation of images, processing of natural languages and video and image analysis. So, the idea of studying the architecture of CNNs has gotten a lot of attention and become popular. This study analysed and contrasted the performance of many different CNN models trained on the publicly accessible Br35h dataset for the detection of brain tumours. These models included the LeNet, AlexNet, VGG16, VGG19 and ResNet50. Several optimisers were used in this research to fine‐tune the performance of the CNN model. These included Adam (adaptive moment estimation), SGD (stochastic gradient descent) and RMSprop (root‐mean‐square propagation). Accuracy, miss‐classification rate, sensitivity, specificity, NPV (negative predictive value), PPV (positive predictive value), F1‐score and false omission rate (FOR) were used to assess the efficacy of five different CNN architectures trained using three different optimisers. The experimental results showed that AlexNet architecture with SGD optimiser performed better than other CNN architecture with different optimisers and achieved the highest accuracy of 98.79% with a miss classification rate of 1.20%. It also achieved 98.98% sensitivity, 98.58% specificity, 98.93% NPV, 98.65% PPV, 98.82% F1‐score and 1.06% FOR.

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Online Learning for X-Ray, CT or MRI

Bhuiyan,

Abdullah Al,

Saif

et al. 2023

Data Driven Approaches on Medical Imaging

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An Overview of Segmentation and Classification Techniques: A Survey of Brain Tumour-Related Research

Cited by 3 publications

References 56 publications

A Review on Occluded Object Detection and Deep Learning-Based Approach in Medical Imaging-Related Research

A Review on Occluded Object Detection and Deep Learning-Based Approach in Medical Imaging-Related Research

Performance analysis of state‐of‐the‐art CNN architectures for brain tumour detection

Online Learning for X-Ray, CT or MRI

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