Convolutional neural network and transfer learning for dose volume histogram prediction for prostate cancer radiotherapy

Ambroa, E.; Perez-Alija, J.; Gallego, P.

doi:10.1016/j.meddos.2021.03.005

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…The model developed in this study as shown in Figure 5 was based on a CNN architecture [33,34] consisting of convolutional layers with batch normalization layers [35] and pooling layers, a rectified linear unit (ReLU) as activation function layers [36], fully connected layers, and then finally with a Softmax function for classification purposes [37]. The difference between a CNN and a traditional artificial neural network [38] is that the former combines a convolutional layer and a pooling layer to perform down-sampling, thus increasing the feature extraction process and subsequently reducing the time required to train the neural network.…”

Section: Deep Convolutional Neural Networkmentioning

confidence: 99%

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

et al. 2022

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In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.

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Section: Deep Convolutional Neural Networkmentioning

confidence: 99%

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

et al. 2022

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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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Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy

Wu,

Jia,

et al. 2024

Clinical Oncology

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Convolutional neural network and transfer learning for dose volume histogram prediction for prostate cancer radiotherapy

Cited by 5 publications

References 31 publications

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

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

Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy

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