Skin cancer is a major public health problem with over 123,000 newly diagnosed cases worldwide in every year. Melanoma is the deadliest form of skin cancer, responsible for over 9000 deaths in the United States each year. Thus, reliable automatic melanoma screening systems would provide a great help for clinicians to detect the malignant skin lesions as early as possible. In the last five years, the efficiency of deep learning-based methods increased dramatically and their performances seem to outperform conventional image processing methods in classification tasks. However, this type of machine learning-based approaches have a main drawback, namely they require thousands of labeled images per classes for their training. In this paper, we investigate how we can create an ensemble of deep convolutional neural networks to improve further their individual accuracies in the task of classifying dermoscopy images into the three classes melanoma, nevus, and seborrheic keratosis when we have no opportunity to train them on adequate number of annotated images. To achieve high classification accuracy, we fuse the outputs of the classification layers of four different deep neural network architectures. More specifically, we propose the aggregation of robust convolutional neural networks (CNNs) into one framework, where the final classification is achieved based on the weighted output of the member CNNs. For aggregation, we consider different fusion-based methods and select the best performing one for this problem. Our experimental results also prove that the creation of an ensemble of different neural networks is a meaningful approach, since each of the applied fusion strategies outperforms the individual networks regarding classification accuracy. The average area under the receiver operating characteristic curve has been found to be 0.891 for the 3-class classification task. For an objective evaluation of our approach, we have tested its performance on the official test database of the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 challenge on Skin Lesion Analysis Towards Melanoma Detection dedicated to skin cancer recognition.
challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top performing participating solutions. We observe that the top performing approaches utilize a blend of clinical information, data augmentation, and the ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
This paper proposes an efficient method for locating the optic disc in retinal images automatically using majority voting scheme and data fusion. We show that instead of inventing a new algorithm which ends up being a minor variation on an old idea, the fusion of different optic disc (OD) detectors can enhance the overall performance of the detection system. The optic disc centre candidates of different optic disc detectors are marked in the image and a circular template is fit on each pixel in the image to count the outputs of these algorithms that fall within the radius. The location with maximum number of optic disc centre candidates is the hotspot and is used to localize the optic disc centre. An assessment of the performance of the combined optic disc detector versus detectors working separately is also presented. Our method achieved highest performance (overall 100% correct detection).
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