scholarly journals Predicting Retinal Diseases using Efficient Image Processing and Convolutional Neural Network (CNN)

2021 ◽  
pp. 221-227
Author(s):  
Asif Mohammad ◽  
Mahruf Zaman Utso ◽  
Shifat Bin Habib ◽  
Amit Kumar Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Retinal Images ◽  
Retinal Diseases ◽  
Medical Sector ◽  
The Neural Network ◽  
Computational Technology

Neural networks in image processing are becoming a more crucial and integral part of machine learning as computational technology and hardware systems are advanced. Deep learning is also getting attention from the medical sector as it is a prominent process for classifying diseases.  There is a lot of research to predict retinal diseases using deep learning algorithms like Convolutional Neural Network (CNN). Still, there are not many researches for predicting diseases like CNV which stands for choroidal neovascularization, DME, which stands for Diabetic Macular Edema; and DRUSEN. In our research paper, the CNN (Convolutional Neural Networks) algorithm labeled the dataset of OCT retinal images into four types: CNV, DME, DRUSEN, and Natural Retina. We have also done several preprocessing on the images before passing these to the neural network. We have implemented different models for our algorithm where individual models have different hidden layers.  At the end of our following research, we have found that our algorithm CNN generates 93% accuracy.

scholarly journals Convolutional neural network for image classification based on transfer learning technique

2021 ◽  
Author(s):  
Ghassan Mohammed Halawani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Learning Networks ◽  
The Neural Network ◽  
Learning Technique ◽  
Common Architecture

The main purpose of this project is to modify a convolutional neural network for image classification, based on a deep-learning framework. A transfer learning technique is used by the MATLAB interface to Alex-Net to train and modify the parameters in the last two fully connected layers of Alex-Net with a new dataset to perform classifications of thousands of images. First, the general common architecture of most neural networks and their benefits are presented. The mathematical models and the role of each part in the neural network are explained in detail. Second, different neural networks are studied in terms of architecture, application, and the working method to highlight the strengths and weaknesses of each of neural network. The final part conducts a detailed study on one of the most powerful deep-learning networks in image classification – i.e. the convolutional neural network – and how it can be modified to suit different classification tasks by using transfer learning technique in MATLAB.

scholarly journals Convolutional neural network for image classification based on transfer learning technique

2021 ◽  
Author(s):  
Ghassan Mohammed Halawani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Learning Networks ◽  
The Neural Network ◽  
Learning Technique ◽  
Common Architecture

The main purpose of this project is to modify a convolutional neural network for image classification, based on a deep-learning framework. A transfer learning technique is used by the MATLAB interface to Alex-Net to train and modify the parameters in the last two fully connected layers of Alex-Net with a new dataset to perform classifications of thousands of images. First, the general common architecture of most neural networks and their benefits are presented. The mathematical models and the role of each part in the neural network are explained in detail. Second, different neural networks are studied in terms of architecture, application, and the working method to highlight the strengths and weaknesses of each of neural network. The final part conducts a detailed study on one of the most powerful deep-learning networks in image classification – i.e. the convolutional neural network – and how it can be modified to suit different classification tasks by using transfer learning technique in MATLAB.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Parvathaneni Naga Srinivasu ◽  
Jalluri Gnana SivaSai ◽  
Muhammad Fazal Ijaz ◽  
Akash Kumar Bhoi ◽  
Wonjoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Network Architecture ◽  
Large Scale ◽  
Short Term Memory ◽  
Convolutional Networks ◽  
Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

scholarly journals Hyperparameters optimization for ResNet and Xception in the purpose of diagnosing COVID-19

2021 ◽  
pp. 1-17
Author(s):  
Hania H. Farag ◽  
Lamiaa A. A. Said ◽  
Mohamed R. M. Rizk ◽  
Magdy Abd ElAzim Ahmed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Random Search ◽  
Learning Networks ◽  
Residual Network ◽  
Global Pandemic ◽  
Search Optimization

COVID-19 has been considered as a global pandemic. Recently, researchers are using deep learning networks for medical diseases’ diagnosis. Some of these researches focuses on optimizing deep learning neural networks for enhancing the network accuracy. Optimizing the Convolutional Neural Network includes testing various networks which are obtained through manually configuring their hyperparameters, then the configuration with the highest accuracy is implemented. Each time a different database is used, a different combination of the hyperparameters is required. This paper introduces two COVID-19 diagnosing systems using both Residual Network and Xception Network optimized by random search in the purpose of finding optimal models that give better diagnosis rates for COVID-19. The proposed systems showed that hyperparameters tuning for the ResNet and the Xception Net using random search optimization give more accurate results than other techniques with accuracies 99.27536% and 100 % respectively. We can conclude that hyperparameters tuning using random search optimization for either the tuned Residual Network or the tuned Xception Network gives better accuracies than other techniques diagnosing COVID-19.

scholarly journals APPLICATION OF CONVOLUTIONAL NEURAL NETWORK FOR DETECTION OF MELANOMA USING SKIN LESION IMAGE ON MOBILE DEVICE

2021 ◽  
Vol 3 (1) ◽  
pp. 8-14
Author(s):  
D. V. Fedasyuk ◽  
◽  
T. V. Demianets ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Mobile Device ◽  
Mobile Application ◽  
High Accuracy ◽  
File Size ◽  
Melanoma Detection ◽  
The Neural Network ◽  
Classification Time

A melanoma is the deadliest skin cancer, so early diagnosis can provide a positive prognosis for treatment. Modern methods for early detecting melanoma on the image of the tumor are considered, and their advantages and disadvantages are analyzed. The article demonstrates a prototype of a mobile application for the detection of melanoma on the image of a mole based on a convolutional neural network, which is developed for the Android operating system. The mobile application contains melanoma detection functions, history of the previous examinations and a gallery with images of the previous examinations grouped by the location of the lesion. The HAM10000-based training dataset has been supplemented with the images of melanoma from the archive of The International Skin Imaging Collaboration to eliminate class imbalances and improve network accuracy. The search for existing neural networks that provide high accuracy was conducted, and VGG16, MobileNet, and NASNetMobile neural networks have been selected for research. Transfer learning and fine-tuning has been applied to the given neural networks to adapt the networks for the task of skin lesion classification. It is established that the use of these techniques allows to obtain high accuracy of the neural network for this task. The process of converting a convolutional neural network to an optimized Flatbuffer format using TensorFlow Lite for placement and use on a mobile device is described. The performance characteristics of the selected neural networks on the mobile device are evaluated according to the classification time on the CPU and GPU and the amount of memory occupied by the file of a single network is compared. The neural network file size was compared before and after conversion. It has been shown that the use of the TensorFlow Lite converter significantly reduces the file size of the neural network without affecting its accuracy by using an optimized format. The results of the study indicate a high speed of application and compactness of networks on the device, and the use of graphical acceleration can significantly decrease the image classification time of the tumor. According to the analyzed parameters, NASNetMobile was selected as the optimal neural network to be used in the mobile application of melanoma detection.

scholarly journals Convolutional Neural Network for Iris Recognition

2021 ◽  
Author(s):  
Wael Alnahari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Iris Recognition ◽  
Recognition System ◽  
High Accuracy ◽  
Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

Using Convolutional Neural Networks for the Detection of Spinal Transverse Processes

2019 ◽  
Author(s):  
Victoria Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transverse Process ◽  
Classification Model ◽  
Ultrasound Images ◽  
Ultrasound Scan ◽  
X Ray ◽  
Scoliosis Screening

Introduction: Scoliosis, an excessive curvature of the spine, affects approximately 1 in 1,000 individuals. As a result, there have formerly been implementations of mandatory scoliosis screening procedures. Screening programs are no longer widely used as the harms often outweigh the benefits; it causes many adolescents to undergo frequent diagnosis X-ray procedure This makes spinal ultrasounds an ideal substitute for scoliosis screening in patients, as it does not expose them to those levels of radiation. Spinal curvatures can be accurately computed from the location of spinal transverse processes, by measuring the vertebral angle from a reference line [1]. However, ultrasound images are less clear than x-ray images, making it difficult to identify the spinal processes. To overcome this, we employ deep learning using a convolutional neural network, which is a powerful tool for computer vision and image classification [2]. Method: A total of 2,752 ultrasound images were recorded from a spine phantom to train a convolutional neural network. Subsequently, we took another recording of 747 images to be used for testing. All the ultrasound images from the scans were then segmented manually, using the 3D Slicer (www.slicer.org) software. Next, the dataset was fed through a convolutional neural network. The network used was a modified version of GoogLeNet (Inception v1), with 2 linearly stacked inception models. This network was chosen because it provided a balance between accurate performance, and time efficient computations. Results: Deep learning classification using the Inception model achieved an accuracy of 84% for the phantom scan.  Conclusion: The classification model performs with considerable accuracy. Better accuracy needs to be achieved, possibly with more available data and improvements in the classification model.  Acknowledgements: G. Fichtinger is supported as a Canada Research Chair in Computer-Integrated Surgery. This work was funded, in part, by NIH/NIBIB and NIH/NIGMS (via grant 1R01EB021396-01A1 - Slicer+PLUS: Point-of-Care Ultrasound) and by CANARIE’s Research Software Program.    Figure 1: Ultrasound scan containing a transverse process (left), and ultrasound scan containing no transverse process (right).                                Figure 2: Accuracy of classification for training (red) and validation (blue). References:           Ungi T, King F, Kempston M, Keri Z, Lasso A, Mousavi P, Rudan J, Borschneck DP, Fichtinger G. Spinal Curvature Measurement by Tracked Ultrasound Snapshots. Ultrasound in Medicine and Biology, 40(2):447-54, Feb 2014.           Krizhevsky A, Sutskeyer I, Hinton GE. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25:1097-1105. 

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

Bio-Medical Image Processing

2020 ◽  
pp. 158-169
Author(s):  
Rasmita Lenka ◽  
Koustav Dutta ◽  
Ashimananda Khandual ◽  
Soumya Ranjan Nayak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Early Detection ◽  
Digital Image Processing ◽  
Medical Science ◽  
Medical Image Processing ◽  
The World ◽  
Medical Reports

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.

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