A Deep Learning Model of Automatic Detection of Pulmonary Nodules Based on Convolution Neural Networks (CNNs)

2016 ◽  
pp. 349-361 ◽  
Author(s):  
Xiaojiao Xiao ◽  
Yan Qiang ◽  
Juanjuan Zhao ◽  
Pengfei Zhao
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Pulmonary Nodules ◽  
Automatic Detection ◽  
Learning Model ◽  
Convolution Neural Networks ◽  
Deep Learning Model

scholarly journals Deep Learning Model for Plant Disease Detection

2020 ◽  
Vol 9 (1) ◽  
pp. 750-754
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Early Detection ◽  
Plant Disease ◽  
Early Stage ◽  
Learning Model ◽  
Plant Diseases ◽  
Convolution Neural Networks ◽  
Deep Learning Model

Food is one of the basic needs of human being. We know that the population is rising enormously.so it is more important to feed such a huge population. But nowadays plants are largely affected with various types of diseases. If proper care should not be taken then it will show effect on quality of food products, quantity and finally on productivity of crops.. so, Early detection of plant disease is very essential, but it is very hard to farmers to monitor the crops manually it takes more processing time, huge amount of work, expensive and need expertised persons. Automatic detection of plant diseases helps the farmers to monitor the large fields easily,because our approach of using convolution neural networks provides a chance to discover diseases at the very early stage. By using Image Processing and machine learning models we can detect the plant diseases automatically but the accuracy is very less, early detection is also a major challenge. With the modern advanced developments in deep learning, in our project we have implemented the convolution neural networks(CNN) which comprises of different layers,by using those layers we can automatically detect and classify the diseases present in the plants. High Classification accuracy and more processing speed are the main advantages of our approach. After training the model on color, grayscale and segmented datasets our deep learning model will be capable of classifying a large number of different diseases and our project gives us the name of the disease that the plant has with its confidence level and also provides remedies for corresponding diseases

EEG-based deep learning model for the automatic detection of clinical depression

2020 ◽  
Vol 43 (4) ◽  
pp. 1349-1360
Author(s):  
Pristy Paul Thoduparambil ◽  
Anna Dominic ◽  
Surekha Mariam Varghese
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Learning Model ◽  
Clinical Depression ◽  
Deep Learning Model

scholarly journals A Novel Deep Convolutional Neural Network for Tuberculosis Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 1135-1139
Author(s):  
Syed Farhan Hyder Abidi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mycobacterium Tuberculosis ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Model

India accounts for the world’s largest number of cases in TB, with 2.8 million cases annually, and accounts for more than a quarter of the global TB burden. Tuberculosis (TB) is caused by the bacterium (Mycobacterium tuberculosis) which most commonly affects the lungs. TB is transmitted from person to person through the air. When people with TB cough, sneeze or spit, the germs are propelled into the air. This paper showcases a methodology which uses a Deep Learning Model (dCNN) for the detection of Tuberculosis in the lungs. The accuracy obtained by the methods for the model is desirable and dependable, which is increasingly productive in contrast to the accuracy shown by other neural networks.

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