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BRAIN

BRAIN TUMOR DETECTION

ayesshaik
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BRAIN

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  1. BRAIN TUMOR DETECTION USING CNN UNDER GUIDENCE OF SAI SATISH SIR, CEO OF INDIAN SERVERS COMPANY, SALAGALA.ABHINA, SHAIK.AYESHA, MACHINE LEARNING BATCH:22-11 TEAM NO:438

  2. CONTENTS: ->INTRODUCTION ->DOMAIN RELATED BACKGROUND ->DATA PREPROCESSING ->DATASET ->DATA AGUMENTATION ->CONCLUSION

  3. INTRODUCTION: . A brain tumor is an abnormal growth of tissue in the brain. The brain is an important organ that controls thought, memory, emotion, touch, motor skills, vision, respiration, body temperature, hunger, and many other processes that regulate our body.

  4. DOMAIN RELATED BACKGROUND: . Deep convolution neural networks, in particular, the encoder-decoder networks, have been extensively used in image segmentation. We develop a deep learning approach for tumor segmentation by combining a modified U-Net and its domain-adapted version (DAU-Net). We divide training samples into two domains according to preliminary segmentation results, and then equip the modified U-Net with domain adaptation structure to obtain a domain invariant feature representation.

  5. DATA PREPROCESSING: .In recent years, deep learning is widely used in medical field for advance disease diagnosis. The purpose of this study is to analyze the effect of data pre-processing techniques on disease classification. The disease considered for the present work is brain tumor. The three different types of brain tumor are Glioma, Meningioma and Pituitary tumor. The motivation of this work is: the diagnosis of the brain tumor type at the early stage may lead to effective treatment. In image processing perspective, there are several methods which solves the disease classification problem. However, one of the recent popular deep learning algorithm known as, Convolutional Neural Networks (CNN) is mainly used for image classification tasks.

  6. DATASET: . The brain tumor dataset contains 2 folders “no” and “yes” with 98 and 155 images each. Load the folders containing the images to our current working directory. Using the imutils module, we extract the paths for all the images and store them in a list called image_paths.

  7. DATA AGUMENTATION: . Data augmentation for brain- tumor segmentation—a taxonomy. Traditionally, data augmentation approaches have been applied to increase the size of training sets, in order to allow large-capacity learners benefit from more representative training data (Wong et al., 2016).

  8. CONCLUSION: . Now the best model,(the one with the best validation accuracy) detects brain tumor with the 0.460317462682724% accuracy on the text set you can find the code in this GitHub repo. Contributes are welcome! I hope you have found this usefull.

  9. THANK YOU SHAIK.AYESHA ayesshaik0516@gmail.com

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