The potential of convolutional neural network diagnosing prostate cancer

Kudo, Maíra Suzuka; Souza, Vinicius Meneguette Gomes de; Amaral, Gabriel de Souza; Melo, Petrônio Augusto de Souza; Estivallet, Carmen Liane Neubarth; Santos, Eric Rocha; Amorim, Henrique Alves de; Moraes, Matheus Cardoso; Leite, Kátia R. M.

doi:10.1007/s42600-020-00095-3

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The data augmentation method has been selected based on previous works (in these studies, it has been shown that the use of these techniques has increased the accuracy of the model.) flipping around vertical or horizontal axes 11 , 12 , 14 , rotation 11 , 12 , 15 – 17 , magnification 14 and displacement (intensity shift) 14 has been used in studies. In this research, with the help of the ImageDataGenerator function from the Keras library and the methods of rotation, symmetry, displacement, and magnification the data have been augmented.…”

Section: Methodsmentioning

confidence: 99%

Prostate cancer diagnosis based on multi-parametric MRI, clinical and pathological factors using deep learning

Sherafatmandjoo,

Safaei,

Ghaderi

et al. 2024

Sci Rep

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Prostate cancer is one of the most common and fatal diseases among men, and its early diagnosis can have a significant impact on the treatment process and prevent mortality. Since it does not have apparent clinical symptoms in the early stages, it is difficult to diagnose. In addition, the disagreement of experts in the analysis of magnetic resonance images is also a significant challenge. In recent years, various research has shown that deep learning, especially convolutional neural networks, has appeared successfully in machine vision (especially in medical image analysis). In this research, a deep learning approach was used on multi-parameter magnetic resonance images, and the synergistic effect of clinical and pathological data on the accuracy of the model was investigated. The data were collected from Trita Hospital in Tehran, which included 343 patients (data augmentation and learning transfer methods were used during the process). In the designed model, four different types of images are analyzed with four separate ResNet50 deep convolutional networks, and their extracted features are transferred to a fully connected neural network and combined with clinical and pathological features. In the model without clinical and pathological data, the maximum accuracy reached 88%, but by adding these data, the accuracy increased to 96%, which shows the significant impact of clinical and pathological data on the accuracy of diagnosis.

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Section: Methodsmentioning

confidence: 99%

Prostate cancer diagnosis based on multi-parametric MRI, clinical and pathological factors using deep learning

Sherafatmandjoo,

Safaei,

Ghaderi

et al. 2024

Sci Rep

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show abstract

“…To ensure methodological similarity, only Radboud images were used to improve the initial training sample. All the samples underwent the same screening process previously presented [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

The value of artificial intelligence for detection and grading of prostate cancer in human prostatectomy specimens: a validation study

et al. 2022

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Background The Gleason grading system is an important clinical practice for diagnosing prostate cancer in pathology images. However, this analysis results in significant variability among pathologists, hence creating possible negative clinical impacts. Artificial intelligence methods can be an important support for the pathologist, improving Gleason grade classifications. Consequently, our purpose is to construct and evaluate the potential of a Convolutional Neural Network (CNN) to classify Gleason patterns. Methods The methodology included 6982 image patches with cancer, extracted from radical prostatectomy specimens previously analyzed by an expert uropathologist. A CNN was constructed to accurately classify the corresponding Gleason. The evaluation was carried out by computing the corresponding 3 classes confusion matrix; thus, calculating the percentage of precision, sensitivity, and specificity, as well as the overall accuracy. Additionally, k-fold three-way cross-validation was performed to enhance evaluation, allowing better interpretation and avoiding possible bias. Results The overall accuracy reached 98% for the training and validation stage, and 94% for the test phase. Considering the test samples, the true positive ratio between pathologist and computer method was 85%, 93%, and 96% for specific Gleason patterns. Finally, precision, sensitivity, and specificity reached values up to 97%. Conclusion The CNN model presented and evaluated has shown high accuracy for specifically pattern neighbors and critical Gleason patterns. The outcomes are in line and complement others in the literature. The promising results surpassed current inter-pathologist congruence in classical reports, evidencing the potential of this novel technology in daily clinical aspects.

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Prostate cancer grading framework based on deep transfer learning and Aquila optimizer

Balaha,

Shaban,

El-Gendy

et al. 2024

Neural Comput & Applic

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Prostate cancer is the one of the most dominant cancer among males. It represents one of the leading cancer death causes worldwide. Due to the current evolution of artificial intelligence in medical imaging, deep learning has been successfully applied in diseases diagnosis. However, most of the recent studies in prostate cancer classification suffers from either low accuracy or lack of data. Therefore, the present work introduces a hybrid framework for early and accurate classification and segmentation of prostate cancer using deep learning. The proposed framework consists of two stages, namely classification stage and segmentation stage. In the classification stage, 8 pretrained convolutional neural networks were fine-tuned using Aquila optimizer and used to classify patients of prostate cancer from normal ones. If the patient is diagnosed with prostate cancer, segmenting the cancerous spot from the overall image using U-Net can help in accurate diagnosis, and here comes the importance of the segmentation stage. The proposed framework is trained on 3 different datasets in order to generalize the framework. The best reported classification accuracies of the proposed framework are 88.91% using MobileNet for the “ISUP Grade-wise Prostate Cancer” dataset and 100% using MobileNet and ResNet152 for the “Transverse Plane Prostate Dataset” dataset with precisions 89.22% and 100%, respectively. U-Net model gives an average segmentation accuracy and AUC of 98.46% and 0.9778, respectively, using the “PANDA: Resized Train Data (512 × 512)” dataset. The results give an indicator of the acceptable performance of the proposed framework.

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The potential of convolutional neural network diagnosing prostate cancer

Cited by 6 publications

References 12 publications

Prostate cancer diagnosis based on multi-parametric MRI, clinical and pathological factors using deep learning

Prostate cancer diagnosis based on multi-parametric MRI, clinical and pathological factors using deep learning

The value of artificial intelligence for detection and grading of prostate cancer in human prostatectomy specimens: a validation study

Prostate cancer grading framework based on deep transfer learning and Aquila optimizer

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