Deep Learning for Brain Tumor Segmentation

Munir, Khushboo; Frezza, Fabrizio; Rizzi, Antonello

doi:10.1007/978-981-15-6321-8_11

Cited by 11 publications

(7 citation statements)

References 51 publications

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“…Another important concern arises in the case of a misdiagnosis, which can cause substantial damage or reduce survival. Many medical diseases have been diagnosed using computer-aided methods, such as malignancies, COVID-19, and others [ 71 , 72 ]. As a result, employing computer-aided approaches to detect cancer is a valid alternative.…”

Section: Imaging Processing Techniques For Brain Tumorsmentioning

confidence: 99%

Deep Learning Hybrid Techniques for Brain Tumor Segmentation

Munir

Frezza

Rizzi

2022

Sensors

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Medical images play an important role in medical diagnosis and treatment. Oncologists analyze images to determine the different characteristics of deadly diseases, plan the therapy, and observe the evolution of the disease. The objective of this paper is to propose a method for the detection of brain tumors. Brain tumors are identified from Magnetic Resonance (MR) images by performing suitable segmentation procedures. The latest technical literature concerning radiographic images of the brain shows that deep learning methods can be implemented to extract specific features of brain tumors, aiding clinical diagnosis. For this reason, most data scientists and AI researchers work on Machine Learning methods for designing automatic screening procedures. Indeed, an automated method would result in quicker segmentation findings, providing a robust output with respect to possible differences in data sources, mostly due to different procedures in data recording and storing, resulting in a more consistent identification of brain tumors. To improve the performance of the segmentation procedure, new architectures are proposed and tested in this paper. We propose deep neural networks for the detection of brain tumors, trained on the MRI scans of patients’ brains. The proposed architectures are based on convolutional neural networks and inception modules for brain tumor segmentation. A comparison of these proposed architectures with the baseline reference ones shows very interesting results. MI-Unet showed a performance increase in comparison to baseline Unet architecture by 7.5% in dice score, 23.91% insensitivity, and 7.09% in specificity. Depth-wise separable MI-Unet showed a performance increase by 10.83% in dice score, 2.97% in sensitivity, and 12.72% in specificity as compared to the baseline Unet architecture. Hybrid Unet architecture achieved performance improvement of 9.71% in dice score, 3.56% in sensitivity, and 12.6% in specificity. Whereas the depth-wise separable hybrid Unet architecture outperformed the baseline architecture by 15.45% in dice score, 20.56% in sensitivity, and 12.22% in specificity.

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Section: Imaging Processing Techniques For Brain Tumorsmentioning

confidence: 99%

Deep Learning Hybrid Techniques for Brain Tumor Segmentation

Munir

Frezza

Rizzi

2022

Sensors

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“…There have been related works on medical image datasets [51][52][53][54], while there are few works for prostate cancer screening. The traditional method of prostate cancer screening usually uses prostate biopsy puncture to obtain the pathological results, which causes great pain for patients.…”

Section: Image Classification Datasets (1) Ultrasonic Image Dataset O...mentioning

confidence: 99%

A Novel Progressive Image Classification Method Based on Hierarchical Convolutional Neural Networks

Cheng

Miao

Gao³

2021

Electronics

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Deep Neural Networks (DNNs) are commonly used methods in computational intelligence. Most prevalent DNN-based image classification methods are dedicated to promoting the performance by designing complicated network architectures and requiring large amounts of model parameters. These large-scale DNN-based models are performed on all images consistently. However, since there are meaningful differences between images, it is difficult to accurately classify all images by a consistent network architecture. For example, a deeper network is fit for the images that are difficult to be distinguished, but may lead to model overfitting for simple images. Therefore, we should selectively use different models to deal with different images, which is similar to the human cognition mechanism, in which different levels of neurons are activated according to the difficulty of object recognition. To this end, we propose a Hierarchical Convolutional Neural Network (HCNN) for image classification in this paper. HCNNs comprise multiple sub-networks, which can be viewed as different levels of neurons in humans, and these sub-networks are used to classify the images progressively. Specifically, we first initialize the weight of each image and each image category, and these images and initial weights are used for training the first sub-network. Then, according to the predicted results of the first sub-network, the weights of misclassified images are increased, while the weights of correctly classified images are decreased. Furthermore, the images with the updated weights are used for training the next sub-networks. Similar operations are performed on all sub-networks. In the test stage, each image passes through the sub-networks in turn. If the prediction confidences in a sub-network are higher than a given threshold, then the results are output directly. Otherwise, deeper visual features need to be learned successively by the subsequent sub-networks until a reliable image classification result is obtained or the last sub-network is reached. Experimental results show that HCNNs can obtain better results than classical CNNs and the existing models based on ensemble learning. HCNNs have 2.68% higher accuracy than Residual Network 50 (Resnet50) on the ultrasonic image dataset, 1.19% than Resnet50 on the chimpanzee facial image dataset, and 10.86% than Adaboost-CNN on the CIFAR-10 dataset. Furthermore, the HCNN is extensible, since the types of sub-networks and their combinations can be dynamically adjusted.

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“…It has a subset named the machine learning (ML) technique that helps to train algorithms in making decisions [2]. Currently, deep learning technology has become a promising approach for clinical detection systems [3]. A deep learning computer-aided diagnosis system has been used to analyze medical images, which has proven to be a remarkable advancement in various medical applications [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Naseer

Akram

Masood

et al. 2022

Sensors

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The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.

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Deep Learning for Brain Tumor Segmentation

Cited by 11 publications

References 51 publications

Deep Learning Hybrid Techniques for Brain Tumor Segmentation

Deep Learning Hybrid Techniques for Brain Tumor Segmentation

A Novel Progressive Image Classification Method Based on Hierarchical Convolutional Neural Networks

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

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