scholarly journals Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network

2020 ◽  
Vol 57 ◽  
pp. 101792 ◽  
Author(s):  
M. Hajabdollahi ◽  
R. Esfandiarpoor ◽  
E. Sabeti ◽  
N. Karimi ◽  
S.M.R. Soroushmehr ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Abnormality Detection ◽  
Medical Image Diagnosis ◽  
Image Diagnosis

scholarly journals Security and Privacy of Cloud- and IoT-Based Medical Image Diagnosis Using Fuzzy Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
J. Deepika ◽  
C. Rajan ◽  
T. Senthil
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Convolutional Neural Network ◽  
Data Storage ◽  
Medical Image ◽  
Disease Classification ◽  
Security And Privacy ◽  
Medical Image Diagnosis ◽  
Image Diagnosis

In recent times, security in cloud computing has become a significant part in healthcare services specifically in medical data storage and disease prediction. A large volume of data are produced in the healthcare environment day by day due to the development in the medical devices. Thus, cloud computing technology is utilised for storing, processing, and handling these large volumes of data in a highly secured manner from various attacks. This paper focuses on disease classification by utilising image processing with secured cloud computing environment using an extended zigzag image encryption scheme possessing a greater tolerance to different data attacks. Secondly, a fuzzy convolutional neural network (FCNN) algorithm is proposed for effective classification of images. The decrypted images are used for classification of cancer levels with different layers of training. After classification, the results are transferred to the concern doctors and patients for further treatment process. Here, the experimental process is carried out by utilising the standard dataset. The results from the experiment concluded that the proposed algorithm shows better performance than the other existing algorithms and can be effectively utilised for the medical image diagnosis.

scholarly journals Medical Image Segmentation by Fish Schooling Algorithm and Neural Network

2021 ◽  
Vol 7 (10) ◽  
pp. 55-60
Author(s):  
K V Sandeep, Manoj Dandamudi and P Dhanusha
Keyword(s):  
Neural Network ◽  
Medical Image ◽  
Tumor Detection ◽  
Medical Image Segmentation ◽  
Detection Accuracy ◽  
Medical Image Diagnosis ◽  
Image Diagnosis ◽  
Tumor Region ◽  
Fish Schooling ◽  
Genetic Feature Selection

Medical image diagnosis by machine decrease the doctor load and increases the efficiency of treatment as well. Many of diagnosis process depends on chemical data and some are depend on digital images. This work focus on brain tumor medical image diagnosis by segmenting the tumor region in the image. For tumor detection neural network was trained by the model. Selected features extract from the image by fish schooling genetic algorithm for training of neural network It was obtained that fish schooling based genetic feature selection has increases the detection accuracy of trained model. Experiment was done on real dataset and results compared with existing techniques of tumor detection from MRI images.

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