Description
In our previous class, we discussed about fundamentals of medical image classification and sematic segmentation using deep neural networks followed by coding example for classification. You should consider multi-class classification. Based on the discussion, please complete the following tasks:
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Develop three classification algorithms using Inception V3, Resnet, and squeezeNet on the following databases:
a. ABIDE Database – Autism classification (40 Marks)
b. HEP2 Cell classification. – (40 Marks)
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Use this paper for dataset details:
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Qi X, Zhao G, Chen J, Pietikäinen M. Exploring illumination robust descriptors for human epithelial type 2 cell classification. Pattern Recognition. 2016 Dec 1;60:420-9.
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Develop an innovative architecture using the principles of Resnet and inception for
improved classification. (20 Marks)
Report to be submitted should include:
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You have to submit the report with the code.
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The report should contain a detailed study on the comparison of the three algorithms and their performances.
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You have to explain the innovative architecture with the details and the choice of such an approach.
Rules:
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Please submit individual assignment
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Please follow the IITH ethics to write assignments
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Plagiarism – strictly not entertained
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Use the template to prepare and submit the answer of the assignment.
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Test the performance of the network trained with the optimal set of parameters on the k-fold cross validation and report the confusion matrix, classification rate and F1 measure per class.
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Think of a reasonable strategy to optimise parameters . Put more effort on optimising parameters you think are more important.
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Make sure that your code runs. If not you will be asked to resubmit the code and lose 20% of the code mark.
