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Deep Learning for Brain Tumor Segmentation: Utilizing ResUNet Architecture
Access all Supporting File from this link:
weights_seg.hdf5
resnet-50-MRI.json
ResUNet-MRI.json
weights.hdf5
Healthcare_AI.ipynb
Healthcare+AI@CoolWaterSlides
data_mask.csv
drive.google.c...
Code File: github.com/sac...
- Data source: www.kaggle.com...
- Clickable link for the full list of startups in AI medical imagery: research.aimul...
- Feature Extraction and Convolutions: setosa.io/ev/i...
- CNN Visualization: www.cs.ryerson...
#AI #MedicalImaging #DeepLearning #CNN #TransferLearning #FeatureExtraction #Convolution #LossFunction #ResUNet #startupsindia
In this video, we delve into the realm of medical imaging and showcase how advanced AI techniques, specifically utilizing the ResUNet deep learning architecture, can be employed for the crucial task of brain tumor segmentation. Leveraging publicly available data from Kaggle, we explore the implementation of a custom deep learning model to accurately detect and localize brain tumors in MRI scans.
We provide insights into the key components of the ResUNet architecture, emphasizing its effectiveness in handling medical image data. Additionally, we discuss the importance of feature extraction, convolutions, and convolutional neural network (CNN) visualization techniques in understanding the inner workings of the model.
One of the critical aspects of our approach is the utilization of a custom loss function tailored for brain tumor segmentation tasks. We draw upon resources from the open-source community, specifically the Focal Tversky loss function, to enhance the training process and improve the model's performance.
Throughout the video, we highlight the significance of transfer learning in medical imaging applications, citing valuable resources by experts such as Dipanjan Sarkar and Jason Brownlee. By leveraging pre-trained models and fine-tuning them for specific tasks, we can achieve superior results with reduced computational overhead.
By the end of this video, viewers will gain a comprehensive understanding of how AI-powered techniques can revolutionize healthcare, particularly in the early detection and precise localization of brain tumors. Join us on this journey as we explore the intersection of deep learning and medical imaging, paving the way for advancements in diagnosis and treatment.
@DataFakta @WatchDataVerified @angkadandata_channel @BossFightDatabase @DuniaDataOfficial @VillageDatabase @DataRmotovlog
- Excellent Resource on transfer learning by Dipanjan Sarkar: towardsdatasci...
- Article by Jason Brownlee: machinelearnin...
#datascience #kaggle Loss function: #machinelearning #atal #python #aiinhealthcare
We need a custom loss function to train this ResUNet.So, we have used the loss function as it is from github.com/nab...
#DeepLearning #MedicalImaging #BrainTumorSegmentation #ResUNet #AIinHealthcare #TransferLearning #FocalTverskyLoss #Kaggle #Neuroscience #MachineLearning
#BrainTumor #Segmentation #NeuralNetworks #MedicalResearch #DataScience
NOW YOU KNOW HOW TO APPLY AI TO DETECT AND LOCALIZE BRAIN TUMORS. THIS IS A GREAT ACHIEVEMENT IN HEALTHCARE.
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Негізгі бет MRI Brain Tumor Segmentation Using ResUNet Deep Learning Architecture
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