ScaledDenseNet: An Efficient Deep Learning Architecture for Skin Lesion Identification (original) (raw)
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Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification
arXiv (Cornell University), 2022
It is generally believed that the human visual system is biased towards the recognition of shapes rather than textures. This assumption has led to a growing body of work aiming to align deep models' decision-making processes with the fundamental properties of human vision. The reliance on shape features is primarily expected to improve the robustness of these models under covariate shift. In this paper, we revisit the significance of shape-biases for the classification of skin lesion images. Our analysis shows that different skin lesion datasets exhibit varying biases towards individual image features. Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation. This indicates that these features are still represented in the learnt embedding spaces of the models, but not used for classification. In addition, the spectral analysis of different datasets shows that in contrast to common visual recognition, dermoscopic skin lesion classification, by nature, is reliant on complex feature combinations beyond shape-bias. As a natural consequence, shifting away from the prevalent desire of shape-biasing models can even improve skin lesion classifiers in some cases.
JMIR Dermatology, 2020
Background Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. Objective We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. Methods We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. Results We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. Conclusions Based on our research, we conclude that deep learning algorithms are hi...
Skin Lesion Detection in Dermatological Images using Deep Learning
Proceedings of Artificial Intelligence for Science, Industry and Society — PoS(AISIS2019)
This paper demonstrates that it is possible to approach the skin lesion classification problem as a detection problem, a much more complex and interesting problem, by training a deep neural network based detection architecture and applying image processing techniques to a dermatology dataset as part of the data augmentation strategy with satisfactory and promising results. The image dataset used in the experiments comes from the ISIC Dermoscopic Archive, an openaccess dermatology repository. In particular, the ISIC 2017 dataset, a subset of the ISIC archive, released for the annual ISIC challenge was used. We show that it is possible to adapt a high quality imaging dataset to the requirements demanded by a deep learning detection architecture such as YOLOv3. In conjunction with image processing techniques as a previous step, the deep neural network was successfully trained to identify and locate three different types of skin lesions in real-time.