A new lightweight convolutional neural network for radiation-induced liver disease classification

2022 ◽  
Vol 73 ◽  
pp. 103463
Author(s):  
Demet Alici-Karaca ◽  
Bahriye Akay ◽  
Arzu Yay ◽  
Pinar Suna ◽  
O. Ufuk Nalbantoglu ◽  
...  
Keyword(s):  
Neural Network ◽  
Liver Disease ◽  
Convolutional Neural Network ◽  
Disease Classification ◽  
Radiation Induced

scholarly journals Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 328
Author(s):  
Sachin B. Jadhav
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Plant Diseases ◽  
Experimental Results ◽  
Disease Classification ◽  
Soybean Leaves ◽  
Soybean Diseases ◽  
Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

scholarly journals Light Intensity Adjustment and Severity Estimation for the Generic Disease of Tomato Plants

2021 ◽  
pp. 353-358
Author(s):  
Faizan Ahmed Sayyad ◽  
Rehan Ahmed Sayyad
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Crop Production ◽  
Disease Classification ◽  
Attention Networks ◽  
Severity Estimation ◽  
Image Capturing ◽  
Severity Of The Disease ◽  
Key Steps

Many research has been done on detecting the type of disease that affects the crops. Because of this the farmers use pesticides to reduce the loss of crop production, since they don’t know how much pesticides to spray or use, they tend to overuse them which eventually leads to further destruction of crops. For disease classification Convolutional Neural Network (CNN) is being used which lets you know what kind of disease has affected the crop. In this paper we have worked on self attention networks to calculate the severity of the disease on the leaf . Self Attention Network introduced in the architecture lets the model learn the feature more efficiently and focus more on the affected region of the leaf. The model was trained and tested on the standard dataset (Plant Village) . The core processes comprises image capturing, image processing and testing on Self Attention Convolutional Neural Network architecture.. All of the key steps required to implement the model are detailed throughout the document.

Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification

2022 ◽  
Vol 73 ◽  
pp. 103406
Author(s):  
R Karthik ◽  
Tejas Sunil Vaichole ◽  
Sanika Kiran Kulkarni ◽  
Ojaswa Yadav ◽  
Faiz Khan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Skin Disease ◽  
Disease Classification

scholarly journals An image-based gangrene disease classification

2020 ◽  
Vol 10 (6) ◽  
pp. 6001
Author(s):  
Pramod Sekharan Nair ◽  
Tsrity Asefa Berihu ◽  
Varun Kumar
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Image Features ◽  
The Body ◽  
Disease Classification ◽  
Support Vector ◽  
Body Parts ◽  
Intermediate Layers ◽  
The Given

Gangrene disease is one of the deadliest diseases on the globe which is caused by lack of blood supply to the body parts or any kind of infection. The gangrene disease often affects the human body parts such as fingers, limbs, toes but there are many cases of on muscles and organs. In this paper, the gangrene disease classification is being done from the given images of high resolution. The convolutional neural network (CNN) is used for feature extraction on disease images. The first layer of the convolutional neural network was used to capture the elementary image features such as dots, edges and blobs. The intermediate layers or the hidden layers of the convolutional neural network extracts detailed image features such as shapes, brightness, and contrast as well as color. Finally, the CNN extracted features are given to the Support Vector Machine to classify the gangrene disease. The experiment results show the approach adopted in this study performs better and acceptable.

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