A deep learning architecture for classifying medical images of anatomy object

Khan, Sameer; Yong, Suet-Peng

doi:10.1109/apsipa.2017.8282299

Cited by 36 publications

(26 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e extracted and pooled features from convolution and pooling layers are mapped in the fully connected layers to the final output. Currently there are many pretrained deep learning CNN models such as Alex Net [51], Res Net [52], Dense Net [53], and VGG net [54] which are available for the classification. As per the literature, the VGG net architecture is the most commonly used deep learning CNN for medical image classification.…”

Section: Svm Classifiermentioning

confidence: 99%

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

Thirunavukkarasu

Snekhalatha

Krishnan

et al. 2020

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

Diabetes mellitus is one of the life threatening diseases over the globe, and an early prediction of diabetes is of utmost importance in this current scenario. International Diabetes Federation (IDF) reported nearly half of the world’s population was undiagnosed and unaware of being developed into diabetes. In 2017, around 84 million individuals were living with diabetes, and it might increase to 156 million by the end of 2045 stated by IDF. Generally, the diagnosis of diabetes relies on the biochemical method that may cause uneasiness and probability of infections to the subjects. To overcome such difficulties, a noninvasive method is much needed around the globe for primary screening. A change in body temperature is an indication of various diseases. Infrared thermal imaging is relatively a novel technique for skin temperature measurement and turned out to be well known in the medical field due to being noninvasive, risk-free, and repeatable. According to traditional Chinese medicine, the human tongue is a sensitive mirror that reflects the body’s pathophysiological condition. So, we have (i) analysed and classified diabetes based on thermal variations at human tongue, (ii) segmented the hot spot regions from tongue thermogram by RGB (red, green, blue) based color histogram image segmentation method and extracted the features using gray level co-occurrence matrix algorithm, (iii) classified normal and diabetes using various machine learning algorithms, and (iv) developed computer aided diagnostic system to classify diabetes mellitus. The baseline measurements and tongue thermograms were obtained from 140 subjects. The measured tongue surface temperature of the diabetic group was found to be greater than normal. The statistical correlation between the HbA1c and the thermal distribution in the tongue region was found to be r2 = 0.5688. The Convolutional Neural Network has outperformed the other classifiers with 94.28% accuracy rate. Thus, tongue thermograms could be used as a preliminary screening approach for diabetes prognosis.

show abstract

Section: Svm Classifiermentioning

confidence: 99%

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

Thirunavukkarasu

Snekhalatha

Krishnan

et al. 2020

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…The learning rate is 0.001. Training 32*32 image is randomly accepted from full image is used , no other data augmentation such as scale jittering is used [23,28]. A network goes deeper problem such as vanishing gradient may become more which make it more difficult to tune the parameter of the earlier layer.…”

Section: Bgooglenetmentioning

confidence: 99%

“…It minimize the number of lower level kernel in a convolutionallayerby dividing color information from real image. Sameer Khan(2017) etal [23] proposed evaluate the three milestone architecture is Lenet,Alexnet and GoogleNet and proposed CNN architecture for classifying medical anatomy images. These models overfit due to the number of layers and hyper parameter used in these architecture has been layer set of natural image and produce better result of image classification.…”

mentioning

confidence: 99%

Pruned Cascade Neural Network Image Classification

Praveenkumar¹,

Dharmalingam²

2019

IJRTE

View full text Add to dashboard Cite

In this paper we propose a new model of deep neural network to build in deeper network. The convoluational neural network is one of the leading Image classification problem. The vanishing gradient problem requires us to use small learning rate with gradient descent which needs many small steps to converge and its take long time to proceed . By using GPU we can process more than one dataset (CIFAR-100) in a particular session. To overcome vanishing gradient problem by using the prune cascade correlation neural network learning algorithm compared to the deep cascade learning in CNN architecture. We improve the filter size, to reduce to the problem by training algorithm that trains in the network from bottom to top approach and its performing attain the task for better image classification in Google Net. We reduce the time complexity (training time ), storage capacity can be used pre training algorithm.

show abstract

“…Given that deep learning tools were successfully applied to image analysis, researchers have explored their application in medical image analysis [1,2,3,4,5,6,7,8]. Deep learning has proven to be a powerful machine learning tool and has demonstrated its ability in automated diagnosis of diseases [2,3,9].…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images

Alaskar

Hussain

Alaseem

et al. 2019

Sensors

142

View full text Add to dashboard Cite

Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and subsequent classification. This research proposes an automated system for detection and classification of ulcers in WCE images, based on state-of-the-art deep learning networks. Deep learning techniques, and in particular, convolutional neural networks (CNNs), have recently become popular in the analysis and recognition of medical images. The medical image datasets used in this study were obtained from WCE video frames. In this work, two milestone CNN architectures, namely the AlexNet and the GoogLeNet are extensively evaluated in object classification into ulcer or non-ulcer. Furthermore, we examine and analyze the images identified as containing ulcer objects to evaluate the efficiency of the utilized CNNs. Extensive experiments show that CNNs deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools.

show abstract

A deep learning architecture for classifying medical images of anatomy object

Cited by 36 publications

References 18 publications

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

Pruned Cascade Neural Network Image Classification

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images

Contact Info

Product

Resources

About