596 research outputs found
Visualizing convolutional neural networks to improve decision support for skin lesion classification
Because of their state-of-the-art performance in computer vision, CNNs are
becoming increasingly popular in a variety of fields, including medicine.
However, as neural networks are black box function approximators, it is
difficult, if not impossible, for a medical expert to reason about their
output. This could potentially result in the expert distrusting the network
when he or she does not agree with its output. In such a case, explaining why
the CNN makes a certain decision becomes valuable information. In this paper,
we try to open the black box of the CNN by inspecting and visualizing the
learned feature maps, in the field of dermatology. We show that, to some
extent, CNNs focus on features similar to those used by dermatologists to make
a diagnosis. However, more research is required for fully explaining their
output.Comment: 8 pages, 6 figures, Workshop on Interpretability of Machine
Intelligence in Medical Image Computing at MICCAI 201
Explainable deep learning models in medical image analysis
Deep learning methods have been very effective for a variety of medical
diagnostic tasks and has even beaten human experts on some of those. However,
the black-box nature of the algorithms has restricted clinical use. Recent
explainability studies aim to show the features that influence the decision of
a model the most. The majority of literature reviews of this area have focused
on taxonomy, ethics, and the need for explanations. A review of the current
applications of explainable deep learning for different medical imaging tasks
is presented here. The various approaches, challenges for clinical deployment,
and the areas requiring further research are discussed here from a practical
standpoint of a deep learning researcher designing a system for the clinical
end-users.Comment: Preprint submitted to J.Imaging, MDP
Deep learning can improve early skin cancer detection
Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy
A Comprehensive Survey of Convolutional Neural Networks for Skin Cancer Classification and Prediction
Skin cancer, a prevalent and potentially fatal condition, requires early detection and precise classification to ensure effective treatment. In recent years, there has been a significant rise in the popularity of Convolutional Neural Networks (CNNs) prominence as a robust solution for image processing and analysis, significantly surpassing conventional techniques in skin cancer prediction and classification. This survey paper offers a thorough examination of CNNs and their diverse applications in diagnosing skin cancer, emphasizing their benefits, existing obstacles, and potential avenues for future research
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