Essays about: "Inception-ResNet-V2"
Found 4 essays containing the word Inception-ResNet-V2.
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1. A Comparison of Convolutional Neural Networks used in Melanoma Detection : With transfer learning on the PAD-UFES-20 and ISIC datasets
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Skin cancer is one of the most common forms of cancer, of which melanoma is the most lethal. Early detection is critical to long term survival rates. The use of machine learning to detect melanoma shows promising results in detecting malignant forms. READ MORE
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2. Detecting Defective Rail Joints on the Swiss Railways with Inception ResNet V2 : Simplifying Predictive Maintenance of Railway Infrastructure
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Manual investigation of railway infrastructure is a labor-intensive and time-consuming task, and automating it has become a high priority for railway operators to reduce unexpected infrastructure expenditure. In this thesis, we propose a new image classification approach for classifying defect and non-defective rail joints in image data, based on previous fault detection algorithms using object detection. READ MORE
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3. Scar detection using deep neural networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Object detection is a computer vision method that deals with the tasks of localizing and classifying objects within an image. The number of usages for the method is constantly growing, and this thesis investigates the unexplored area of using deep neural networks for scar detection. READ MORE
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4. Transfer learning between domains : Evaluating the usefulness of transfer learning between object classification and audio classification
University essay from Högskolan i Skövde/Institutionen för informationsteknologiAbstract : Convolutional neural networks have been successfully applied to both object classification and audio classification. The aim of this thesis is to evaluate the degree of how well transfer learning of convolutional neural networks, trained in the object classification domain on large datasets (such as CIFAR-10, and ImageNet), can be applied to the audio classification domain when only a small dataset is available. READ MORE
