Attention-guided deep convolutional neural networks for skin cancer classification

AbstractIn this work, we address the problem of skin cancer classification using convolutional neural networks. A lot of cancer cases early on are misdiagnosed as something else leading to severe consequences including the death of a patient. Also there are cases in which patients have some other problems and doctors think they might have skin cancer. This leads to unnecessary time and money spent for further diagnosis. In this work, we address both of the above problems using deep neural networks and transfer learning architecture. We have used publicly available ISIC databases for both training and testing our model. Our work achieves an accuracy of 0.935, precision of 0.94, recall of 0.77, F1 score of 0.85 and ROC-AUC of 0.861 which is better than the previous state of the art approaches.

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Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Journal of Medical Internet Research ◽

10.2196/11936 ◽

2018 ◽

Vol 20 (10) ◽

pp. e11936 ◽

Cited By ~ 80

Author(s):

Titus Josef Brinker ◽

Achim Hekler ◽

Jochen Sven Utikal ◽

Niels Grabe ◽

Dirk Schadendorf ◽

...

Keyword(s):

Systematic Review ◽

Neural Networks ◽

Skin Cancer ◽

Convolutional Neural Networks ◽

Cancer Classification

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AUTOMATIC DIAGNOSIS OF BREAST CANCER IN HISTOLOGY IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORKS

KỶ YẾU HỘI NGHỊ KHOA HỌC CÔNG NGHỆ QUỐC GIA LẦN THỨ XI NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN ◽

10.15625/vap.2018.0009 ◽

2018 ◽

Author(s):

Hung Le Minh ◽

Manh Mai Van ◽

Toan Tran Dinh ◽

Tot Tran Dac ◽

Tran Van Lang

Keyword(s):

Breast Cancer ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Automatic Diagnosis

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CNN-based Classification of Degraded Images

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-028 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 28-1-28-7 ◽

Cited By ~ 1

Author(s):

Kazuki Endo ◽

Masayuki Tanaka ◽

Masatoshi Okutomi

Keyword(s):

Neural Networks ◽

Image Restoration ◽

Image Classification ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Alternative Approach ◽

Degraded Image ◽

Degraded Images ◽

Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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