Early Detection of Brain Tumor and Classification of MRI Images Using Convolution Neural Networks

2019 ◽  
pp. 427-436
Author(s):  
Kumbham Bhargavi ◽  
Jangam J. S. Mani
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Early Detection ◽  
Convolution Neural Networks

Early Detection of Disease in Rice Paddy: A Deep Learning based Convolution Neural Networks Approach

2021 ◽  
Author(s):  
Anghsuman Chakraborty ◽  
Soumik Layek ◽  
Ravi Sankar ◽  
Sangit Saha ◽  
Alokesh Ghosh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Early Detection ◽  
Rice Paddy ◽  
Early Detection Of Disease ◽  
Convolution Neural Networks

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

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