Automated Classification Map Generation of Prostate Cancer using Deep Learning

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

Download Full-text

An integrated nomogram combining deep learning, Prostate Imaging–Reporting and Data System (PI-RADS) scoring, and clinical variables for identification of clinically significant prostate cancer on biparametric MRI: a retrospective multicentre study

The Lancet Digital Health ◽

10.1016/s2589-7500(21)00082-0 ◽

2021 ◽

Vol 3 (7) ◽

pp. e445-e454

Author(s):

Amogh Hiremath ◽

Rakesh Shiradkar ◽

Pingfu Fu ◽

Amr Mahran ◽

Ardeshir R Rastinehad ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Multicentre Study ◽

Data System ◽

Clinical Variables ◽

Prostate Imaging ◽

Clinically Significant

Download Full-text

Prostate Cancer Detection Using Deep Learning and Traditional Techniques

IEEE Access ◽

10.1109/access.2021.3057654 ◽

2021 ◽

Vol 9 ◽

pp. 27085-27100

Author(s):

Saqib Iqbal ◽

Ghazanfar Farooq Siddiqui ◽

Amjad Rehman ◽

Lal Hussain ◽

Tanzila Saba ◽

...

Keyword(s):

Prostate Cancer ◽

Deep Learning ◽

Cancer Detection ◽

Prostate Cancer Detection

Download Full-text

Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽

10.3390/e23060667 ◽

2021 ◽

Vol 23 (6) ◽

pp. 667

Author(s):

Wei Chen ◽

Qiang Sun ◽

Xiaomin Chen ◽

Gangcai Xie ◽

Huiqun Wu ◽

...

Keyword(s):

Neural Network ◽

Systematic Review ◽

Deep Learning ◽

Heart Sounds ◽

Learning Approaches ◽

Automated Classification ◽

Learning Methods ◽

Artificial Intelligence Technology ◽

Medical Big Data ◽

Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

Download Full-text

Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

Diagnostics ◽

10.3390/diagnostics11030518 ◽

2021 ◽

Vol 11 (3) ◽

pp. 518

Author(s):

Da-Chuan Cheng ◽

Te-Chun Hsieh ◽

Kuo-Yang Yen ◽

Chia-Hung Kao

Keyword(s):

Breast Cancer ◽

Prostate Cancer ◽

Deep Learning ◽

Bone Metastasis ◽

Cancer Patients ◽

Bone Scintigraphy ◽

Breast Cancer Patients ◽

Location Detection ◽

The Mean ◽

Mean Sensitivity

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.

Download Full-text