Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier

2018 ◽  
Vol 256 (11) ◽  
pp. 2053-2060 ◽  
Author(s):  
Maximilian Treder ◽  
Jost Lennart Lauermann ◽  
Nicole Eter
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Geographic Atrophy ◽  
Deep Convolutional Neural Network ◽  
Neural Network Classifier

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

scholarly journals Exploration of deep-learning based classification with human SNP image graphs

2021 ◽  
Author(s):  
Chao-Hsin Chen ◽  
Kuo-Fong Tung ◽  
Wen-Chang Lin
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Validation Dataset ◽  
Phenotypic Traits ◽  
Human Race ◽  
Large Numbers ◽  
Image Graphs

AbstractBackgroundWith the advancement of NGS platform, large numbers of human variations and SNPs are discovered in human genomes. It is essential to utilize these massive nucleotide variations for the discovery of disease genes and human phenotypic traits. There are new challenges in utilizing such large numbers of nucleotide variants for polygenic disease studies. In recent years, deep-learning based machine learning approaches have achieved great successes in many areas, especially image classifications. In this preliminary study, we are exploring the deep convolutional neural network algorithm in genome-wide SNP images for the classification of human populations.ResultsWe have processed the SNP information from more than 2,500 samples of 1000 genome project. Five major human races were used for classification categories. We first generated SNP image graphs of chromosome 22, which contained about one million SNPs. By using the residual network (ResNet 50) pipeline in CNN algorithm, we have successfully obtained classification models to classify the validation dataset. F1 scores of the trained CNN models are 95 to 99%, and validation with additional separate 150 samples indicates a 95.8% accuracy of the CNN model. Misclassification was often observed between the American and European categories, which could attribute to the ancestral origins. We further attempted to use SNP image graphs in reduced color representations or images generated by spiral shapes, which also provided good prediction accuracy. We then tried to use the SNP image graphs from chromosome 20, almost all CNN models failed to classify the human race category successfully, except the African samples.ConclusionsWe have developed a human race prediction model with deep convolutional neural network. It is feasible to use the SNP image graph for the classification of individual genomes.

scholarly journals Multi- class classification of breast cancer abnormalities using Deep Convolutional Neural Network (CNN)

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256500
Author(s):  
Maleika Heenaye-Mamode Khan ◽  
Nazmeen Boodoo-Jahangeer ◽  
Wasiimah Dullull ◽  
Shaista Nathire ◽  
Xiaohong Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Learning Rate ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Model

The real cause of breast cancer is very challenging to determine and therefore early detection of the disease is necessary for reducing the death rate due to risks of breast cancer. Early detection of cancer boosts increasing the survival chance up to 8%. Primarily, breast images emanating from mammograms, X-Rays or MRI are analyzed by radiologists to detect abnormalities. However, even experienced radiologists face problems in identifying features like micro-calcifications, lumps and masses, leading to high false positive and high false negative. Recent advancement in image processing and deep learning create some hopes in devising more enhanced applications that can be used for the early detection of breast cancer. In this work, we have developed a Deep Convolutional Neural Network (CNN) to segment and classify the various types of breast abnormalities, such as calcifications, masses, asymmetry and carcinomas, unlike existing research work, which mainly classified the cancer into benign and malignant, leading to improved disease management. Firstly, a transfer learning was carried out on our dataset using the pre-trained model ResNet50. Along similar lines, we have developed an enhanced deep learning model, in which learning rate is considered as one of the most important attributes while training the neural network. The learning rate is set adaptively in our proposed model based on changes in error curves during the learning process involved. The proposed deep learning model has achieved a performance of 88% in the classification of these four types of breast cancer abnormalities such as, masses, calcifications, carcinomas and asymmetry mammograms.

Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network

2019 ◽  
Vol 30 (2) ◽  
pp. 1264-1273 ◽  
Author(s):  
Jeong Hyun Lee ◽  
Ijin Joo ◽  
Tae Wook Kang ◽  
Yong Han Paik ◽  
Dong Hyun Sinn ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Liver Fibrosis ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Automated Classification

scholarly journals Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images

2022 ◽  
Author(s):  
Xiaofeng Xie ◽  
Chi-Cheng Fu ◽  
Lei Lv ◽  
Qiuyi Ye ◽  
Yue Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Learning ◽  
Pleural Effusion ◽  
Convolutional Neural Network ◽  
Cancer Cells ◽  
Deep Convolutional Neural Network ◽  
Learning Method ◽  
Weakly Supervised

AbstractLung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses. In this study, we performed a weakly supervised deep learning method for the classification of benign and malignant cells in lung cytological images through a deep convolutional neural network (DCNN). A total of 404 cases of lung cancer cells in effusion cytology specimens from Shanghai Pulmonary Hospital were investigated, in which 266, 78, and 60 cases were used as the training, validation and test sets, respectively. The proposed method was evaluated on 60 whole-slide images (WSIs) of lung cancer pleural effusion specimens. This study showed that the method had an accuracy, sensitivity, and specificity respectively of 91.67%, 87.50% and 94.44% in classifying malignant and benign lesions (or normal). The area under the receiver operating characteristic (ROC) curve (AUC) was 0.9526 (95% confidence interval (CI): 0.9019–9.9909). In contrast, the average accuracies of senior and junior cytopathologists were 98.34% and 83.34%, respectively. The proposed deep learning method will be useful and may assist pathologists with different levels of experience in the diagnosis of cancer cells on cytological pleural effusion images in the future.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

Sign in / Sign up

Export Citation Format

Share Document