Skin cancer is one of the most common cancers, and its early detection can have a huge impact on outcomes. Deep learning, especially convolutional neural networks, perform well in processing massive amounts data, image data classifying skin cancer. In this paper, networks are mainly used to diagnose classify 7 types lesions, including melanoma, basal cell carcinoma, melanocytic nevus, actinic k...

We investigate Support Vector Machines (SVM ) in the context of oral lesion classification using digital color images as input. Two common lesions of similar visual appearance to the human observer were evaluated: oral leukoplakia, which is a potentially pre-cancerous lesion, and oral lichenoid reactions (with subclasses of atrophic, plaqueformed and reticular reactions), which are usually harm...

We proposed a two stage framework with only one network to analyze skin lesion images, we firstly trained a convolutional network to classify these images, and cropped the import regions which the network has the maximum activation value. In the second stage, we retrained this CNN with the image regions extracted from stage one and output the final probabilities. The two stage framework achieve...

OBJECTIVES The utility of a depth of lesion classification using an SPGR MRI sequence in children with moderate to severe traumatic brain injury (TBI) was examined. Clinical and depth of lesion classification measures of TBI severity were used to predict neurological and functional outcome after TBI. METHODS One hundred and six children, aged 4 to 19, with moderate to severe TBI admitted to a...

Melanoma, a malignant form of skin cancer is very threatening to life. Diagnosis of melanoma at an earlier stage is highly needed as it has a very high cure rate. Benign and malignant forms of skin cancer can be detected by analyzing the lesions present on the surface of the skin using dermoscopic images. In this work, an automated skin lesion detection system has been developed which learns th...

This paper presents a hierarchical classification system based on the kNearest Neighbors (kNN) classifier for classification of ten different classes of Malignant and Benign skin lesions from color image data. Our key contribution is to focus on the ten most common classes of skin lesions. There are five malignant: Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC...

Within medical imaging, manual curation of sufficient welllabeled samples is cost, time and scale-prohibitive. To improve the representativeness of the training dataset, for the first time, we present an approach to utilize large amounts of freely available web data through web-crawling. To handle noise and weak nature of web annotations, we propose a two-step transfer learning based training p...