A two-stage CNN method for MRI image segmentation of prostate with lesion

Wang, Zixuan; Wu, Ruofan; Xu, Yanran; Liu, Yi; Chai, Ruimei; Ma, He

doi:10.1016/j.bspc.2023.104610

Cited by 21 publications

(5 citation statements)

References 14 publications

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“…In addition, compared to other neural networks, CNNs are significantly easier to construct on a large scale [32]. In application, CNNs have widespread applications in various fields, such as disease detection and classification [2,9,10,11,34], transportation [35], facial recognition [36], and speech recognition [37]. A common architecture structure of CNNs is convolutional layers followed by subsampling (pooling) layers followed by FC layers [32].…”

Section: Convolution Neural Network (Cnn)mentioning

confidence: 99%

“…In fact, the CNN technique has extensive applications in disease diagnosis. The performance of CNNs in a variety of cancer detection and classification tasks, such as breast cancer [10], prostate cancer [11], liver lesions [12], and lung cancer [13], is particularly encouraging. According to Sudharshan et al [14], employing CNN models enhances the performance of diagnostic systems.…”

Section: Introductionmentioning

confidence: 99%

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Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Saleh,

Hassan,

Salaheldin

2024

Preprint

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Skin cancer can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and classification. Considering the development of multiple models according to various scenarios and selecting the optimum model, these models were rarely considered in previous works. This study aimed to develop multiple models for skin cancer classification and select the optimum model. Convolutional neural networks (CNNs) in the form of AlexNet, Inception V3, MobileNet V2, and ResNet 50 were used for feature extraction. Feature reduction was carried out using two algorithms of the gray wolf optimizer (GWO) in addition to using the original features. Skin cancer images were classified into four classes based on six machine learning (ML) classifiers. As a result, 51 models were developed with different combinations of CNN algorithms, without GWO algorithms, with two GWO algorithms, and with six ML classifiers. To select the optimum model with the best results, the multicriteria decision-making approach was utilized in the recent form of ranking the alternatives by perimeter similarity (RAPS). Model training and testing were conducted using the International Skin Imaging Collaboration (ISIC) 2017 dataset. Based on nine evaluation metrics and according to the RAPS method, the AlexNet algorithm with GWO yielded the optimum model, achieving a classification accuracy of 94.5%. This work presents the first study on benchmarking skin cancer classification with a large number of models. Feature reduction not only reduces the time spent on training but also improves classification accuracy. The RAPS method has proven its robustness in the problem of selecting the best model for skin cancer classification.

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Section: Convolution Neural Network (Cnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Saleh,

Hassan,

Salaheldin

2024

Preprint

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“…Fully automatic segmentation is expected to become the dominant method soon [48]. For example, CNNs have been widely employed for automatic segmentation [49,50]. In [51], they developed a multiregional automatic segmentation model based on CNNs using the intercontinental queue of PCa MRI.…”

Section: Segmentationmentioning

confidence: 99%

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

Chaddad

Tan

Liang

et al. 2023

Cancers

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The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

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“…Prostate cancer is one of the common types of cancer in men, and it is estimated that 1 out of 9 men will be diagnosed with prostate cancer at some point during their lifetime [ 1 – 3 ]. Prostate cancer can often be treated successfully if it is detected early, so it is important for men to get regular screenings to check for any signs or symptoms [ 4 – 8 ]. AI techniques are being used to detect prostate cancer to improve accuracy and reduce costs, such as Machine Learning (ML) and Deep Learning (DL), which are used to analyze MRI scans and CT scans to analyze patient data such as age, race, family history, and lifestyle factors.…”

Section: Introductionmentioning

confidence: 99%

Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture

Talaat,

El-Sappagh,

Alnowaiser

et al. 2024

BMC Med Inform Decis Mak

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Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis, particularly in cases where prostate imaging is challenging. In this paper, we propose a Prostate Cancer Detection Model (PCDM) model for the automatic diagnosis of prostate cancer. It proves its clinical applicability to aid in the early detection and management of prostate cancer in real-world healthcare environments. The PCDM model is a modified ResNet50-based architecture that integrates faster R-CNN and dual optimizers to improve the performance of the detection process. The model is trained on a large dataset of annotated medical images, and the experimental results show that the proposed model outperforms both ResNet50 and VGG19 architectures. Specifically, the proposed model achieves high sensitivity, specificity, precision, and accuracy rates of 97.40%, 97.09%, 97.56%, and 95.24%, respectively.

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A two-stage CNN method for MRI image segmentation of prostate with lesion

Cited by 21 publications

References 14 publications

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture

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