Deep learning approach for microarray image analysis
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To identify the level of gene expression
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Deep learning approach for microarray image analysis
- 1. DEEP LEARNING APPROACH FOR MICROARRAY IMAGE ANALYSIS
- 3. OBJECTIVE • IDENTIFY THE COLOUR INTENSITY OF GENE EXPRESSION • SINGLE MICROARRAY IMAGE CONTAINS LOT OF GENE EXPRESSION • EVERY GENE EXPRESSION IS ANALYSED USING DEEP LEARNING
- 4. STACKED SPACE AUTO ENCODER • THE STACKED AUTOENCODER IS A NEURAL NETWORK CONSISTING OF MULTIPLE LAYERS OF BASIC SPACE AUTO ENCODER IN WHICH THE OUTPUTS OF EACH LAYER ARE WIRED TO THE INPUTS OF EACH SUCCESSIVE LAYER
- 5. WHY SSAE APPROACH , NOT CNN ? • CONVULTION NEURAL NETWORK, CNN COMPARES THE DIFFERENCE BETWEEN THE PATCHES OF THE IMAGES WHEREAS IN THIS CASE IT IS HAVING CLOSELY SIMILAR IMAGES HENCE INORDER TO DIFFERENTIATE THOSE IMAGE PATCHES DEEP LEARNING APPROACH SSAE IS FAVOURED
- 7. FEATURE SELECTION • AUTOENCODER ASSUME A WEIGHT AND RANDOMLY FOLLOW ANY ALGORITHM • DECODER TRY TO REPRODUCE THE IMAGE BY USING THE WEIGHTS • IF THE IMAGE WAS REPRODUCED THEN IT FOLLOWS THE ALGORITHM • ELSE IT CHANGES THE WEIGHT AND FOLLOW NEW ALGORITHM
- 10. WORK FLOW • DATASET COLLECTION • PARAMETER SETTING • TRAINING DATASETS • GROUND TRUTH GENERATION • SSAE+SMC • ZERO COST FUNCTION
- 11. PARAMETER SETTING • EPOCH – NUMBER OF ITERATIONS IMAGE TO BE PROCEESED • WEIGHT- EFFECT OF A FEATURE ON IMAGE • BATCH SIZE • RESCALING
- 12. DATA NORMALIZATION • FOR IMAGE INPUT IN FORM OF MATRIX • 2D TO SPLIT THE MATRIX AND FINALLY REDUCE FORM IS THERE • SUBTRACTING THE VALUE FROM ITS MEAN • DIVIDING BY ITS VARIANCE
- 13. TRAINING DATASET • MAKING THE MACHINE TO UNDERSTAND THE PICTURE • BASED ON THE THRESHOLD VALUE NEURON GET ACTIVATED • BASED ON SUPERVISED LEARNING
- 14. GROUND TRUTH TABLE • SUMMATION WAS DONE IN ANN • THRESHOLD VALUE WAS FIXED AND ACTIVATION FUNCTION WAS USED • TO GENERATE OUTPUT 0/1 • 0 INDICATES HEALTHY PERSON • 1 INDICATES CANCER PATIENT
- 15. SMC • SOFTMAX CLASSIFIER- ACTIVATION FUNCTION • WEIGHTS TO SMC ARE ASSIGNED FROM TRAINING DATASET • SLIDING WINDOW SELECT EACH IMAGE PATCH • FEEDING THIS TO SMC OUTPUT 0-1 • BASED ON VALUE THE PREDICTION CAN BE DONE