Greetings!
I want to create Artificial Neural Network in MATLAB version r2015a for recognition of 8 classes of bacteria images.
Genuinely i'm having some hard times of extracting in the image processing task in MATLAB, correctly to extract the following features but with which method - method based on the threshold pixel values in the binarization of the images or using edge detection first and then to use Generalized Hough Transformation to obtain the desired shape, but honestly i dont know which approach to take with the methods i've mention.
For the data splitting of the extracted features with cvpartition.
The desired ANN architectures i'm planning to use are:
1. feedforwardnet (backprogragation ANN with Gradient descent , MSE error)
2. Cascade feedforward network
Also i'm interested to use The Cascaded-Correlation Learning Architecture.
Also is there any information out there that explains the GUI in MATLAB when the Neural Network completes the training , i want to learn more about how performance window works, Plot regression , Error histogram , Confussion matrix.
Thanks for your time!
Dear George Todorov ,
The ability of bacteria to recognize kin provides a means to form social groups. In turn these groups can lead to cooperative behaviors that surpass the ability of the individual. Kin recognition involves specific biochemical interactions between a receptor(s) and an identification molecule(s). To ensure that nonkin are excluded and kin are included, recognition specificity is critical and depends on the number of loci and polymorphisms involved.
Article Kin Recognition in Bacteria
Regards,
Shafagat
Dear George Todorov ,
Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria types from microscopy images is time-consuming and a challenging task for even experienced biologists. In this study, an automated deep learning based classification approach has been proposed to classify bacterial images into different categories. The ResNet-50 pre-trained CNN architecture has been used to classify digital bacteria images into 33 categories.
Preprint An Automated Deep Learning Approach for Bacterial Image Classification
Regards,
Shafagat
Greetings Shafagat Mahmudova ,
I'm really thankful for your answers and recommendation !
For my task i need to use Artificial Neural Networks.
I was struggling with obtaining the features of microscope and other kind of segmented binary images presenting the bacterias.
As an inspiration and trying to follow: Xiaojuan, Li & Cunshe, Chen. (2007). A novel bacteria classification scheme based on microscopic image. Proc. of the 6th Conference on WSEAS Int. Conf. on Applied Computer Science, vol. 6. 6.
Now with getting into Shape descriptors - Global/Region-based for Image processing , i'm getting to catch up their idea.
My task involves a basic recognition because of the limitations of Artificial Neural Network, features of the images in the database need to be extracted. Feature data to be split into training/target/testing set.
The aim for my task is basic recognition/ classification output 0 ( doesn't belong to the class) 1 (It belongs to the class) .
Cause i'm getting into machine learning , first i need to understand the concept of ANN , then i could go with Deep Learning in future again with bacteria task so i'm really thankful for your recommendation for a Deep learning approach !
Best regards,
George
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