Skin Lesion Classification Using Convolutional Neural Network With Novel Regularizer

Albahar, Marwan Ali

doi:10.1109/access.2019.2906241

Cited by 199 publications

(103 citation statements)

References 37 publications

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“…e neural network is able to model the high-level relationship among features. It is powerful in a variety of application scenarios [18][19][20]. For example, in the tag recommendation task, Yuan et al utilized the multilayer perceptron to model the nonlinearities of interactions among users, items, and tags [21].…”

Section: Neural Network Internet Addiction (Nia) Modelmentioning

confidence: 99%

Intelligent Behavior Data Analysis for Internet Addiction

Peng

Zhang

2019

Scientific Programming

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Internet addiction refers to excessive internet use that interferes with daily life. Due to its negative impact on college students’ study and life, discovering students’ internet addiction tendencies and making correct guidance for them timely is necessary. However, at present, the research methods used in analyzing students’ internet addiction are mainly questionnaires and statistical analysis, which relies on the domain experts heavily. Fortunately, with the development of the smart campus, students’ behavior data such as consumption and trajectory information in the campus are stored. With this information, we can analyze students’ internet addiction levels quantitatively. In this paper, we provide an approach to estimate college students’ internet addiction levels using their behavior data in the campus. In detail, we consider students’ addiction towards the internet is a hidden variable which affects students’ daily time online together with other behavior. By predicting students’ daily time online, we will find students’ internet addiction levels. Along this line, we develop a linear internet addiction (LIA) model, a neural network internet addiction (NIA) model, and a clustering-based internet addiction (CIA) model to calculate students’ internet addiction levels, respectively. These three models take the regularity of students’ behavior and the similarity among students’ behavior into consideration. Finally, extensive experiments are conducted on a real-world dataset. The experimental results show the effectiveness of our method, and it is also consistent with some psychological findings.

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Section: Neural Network Internet Addiction (Nia) Modelmentioning

confidence: 99%

Intelligent Behavior Data Analysis for Internet Addiction

Peng

Zhang

2019

Scientific Programming

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“…A Malignant tumor is a disorder in the human body in which unusual cells divide uncontrollably and destroy body tissue [1]. One of the prevailing malignancies in humans today is skin cancer [2] and this has been stated to be widespread in some parts of the world [3]- [6]. Among various categories of skin cancer [7]- [9], melanoma is the most deadly and dangerous form of cancer [3].…”

Section: Introductionmentioning

confidence: 99%

“…Timely identification and diagnosis of skin cancer can cure nearly 95% of cases [10]. Primarily, this disease is diagnosed visually via clinical screening and analysis of dermoscopic, biopsy, and histopathological images [2] [10]. However, accurate diagnosis of skin lesions using these techniques is difficult, time-consuming, and error-prone even for experienced radiologists; considering the heterogeneous appearances, irregular shapes, and boundaries of the skin lesion lesions [11] as shown in Fig.1.…”

Section: Introductionmentioning

confidence: 99%

FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images

2020

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Skin Lesion detection and classification are very critical in diagnosing skin malignancy. Existing Deep learning-based Computer-aided diagnosis (CAD) methods still perform poorly on challenging skin lesions with complex features such as fuzzy boundaries, artifacts presence, low contrast with the background and, limited training datasets. They also rely heavily on a suitable turning of millions of parameters which often leads to over-fitting, poor generalization, and heavy consumption of computing resources. This study proposes a new framework that performs both segmentation and classification of skin lesions for automated detection of skin cancer. The proposed framework consists of two stages: the first stage leverages on an encoder-decoder Fully Convolutional Network (FCN) to learn the complex and inhomogeneous skin lesion features with the encoder stage learning the coarse appearance and the decoder learning the lesion borders details. Our FCN is designed with the sub-networks connected through a series of skip pathways that incorporate long skip and shortcut connections unlike, the only long skip connections commonly used in the traditional FCN, for residual learning strategy and effective training. The network also integrates the Conditional Random Field (CRF) module which employs a linear combination of Gaussian kernels for its pairwise edge potentials for contour refinement and lesion boundaries localization. The second stage proposes a novel FCN-based DenseNet framework that is composed of dense blocks that are merged and connected via the concatenation strategy and transition layer. The system also employs hyperparameters optimization techniques to reduce network complexity and improve computing efficiency. This approach encourages feature reuse and thus requires a small number of parameters and effective with limited data. The proposed model was evaluated on publicly available HAM10000 dataset of over 10000 images consisting of 7 different categories of diseases with 98% accuracy, 98.5% recall, and 99% of AUC score respectively.

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“…Then, automatic methods were build to Lung diseases on CT images [10,11]. Moreover, similar methods were also created to analyze skin diseases from skin image [12][13][14][15][16][17][18][19][20][21]. If we take a look in more detail on the method, deep learning has been chosen recently and massively as one of the methods for automatically analyzing the medical image [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Medical vision: web and mobile medical image retrieval system based on google cloud vision

Putra

Asra

Hardijaya

et al. 2020

IJECE

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The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image.

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Skin Lesion Classification Using Convolutional Neural Network With Novel Regularizer

Cited by 199 publications

References 37 publications

Intelligent Behavior Data Analysis for Internet Addiction

Intelligent Behavior Data Analysis for Internet Addiction

FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images

Medical vision: web and mobile medical image retrieval system based on google cloud vision

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