A comprehensive survey on image-based computer aided diagnosis systems for skin cancer

Hameed, Nazia; Ruskin, Anglia; Abuhassan, Kamal; Hossain, Mohammad Amzad

doi:10.1109/skima.2016.7916221

Cited by 53 publications

(28 citation statements)

References 36 publications

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“…Pre-processing, segmentation, feature extraction and classification are the key phases of the CAD system for medical image classification [10]. The classification scheme for multi-class skin lesions classification is graphically illustrated in Figure 2, which comprises the phases of preprocessing, segmentation, feature extraction and classification.…”

Section: Methodsmentioning

confidence: 99%

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An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Hameed

Shabut

et al. 2019

Computers

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Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions.

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Section: Methodsmentioning

confidence: 99%

“…Eczema causes the skin to be red, sored, dry and cracked [9]. Malignant melanoma; a kind of skin cancer is a fatal disease and caused by the excessive growth of melanin in melanocytic cells [10]. Malignant melanoma is treatable if detected in the early stages.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Hameed

Shabut

et al. 2019

Computers

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“…The human skin is the largest human organ, and it acts as a barrier between the human body and microbes as well as pathogens [1]. When this barrier breaches and the harmful environmental is associated with them.…”

Section: Introductionmentioning

confidence: 99%

Mobile-based Skin Lesions Classification Using Convolution Neural Network

Hameed¹,

Shabut²,

Hameed³

et al. 2020

AETiC

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This research work is aimed at investing skin lesions classification problem using Convolution Neural Network (CNN) using cloud-server architecture. Using the cloud services and CNN, a real-time mobile-enabled skin lesions classification expert system “i-Rash” is proposed and developed. i-Rash aimed at early diagnosis of acne, eczema and psoriasis at remote locations. The classification model used in the “i-Rash” is developed using the CNN model “SqueezeNet”. The transfer learning approach is used for training the classification model and model is trained and tested on 1856 images. The benefit of using SqueezeNet results in a limited size of the trained model i.e. only 3 MB. For classifying new image, cloud-based architecture is used, and the trained model is deployed on a server. A new image is classified in fractions of seconds with overall accuracy, sensitivity and specificity of 97.21%, 94.42% and 98.14% respectively. i-Rash can serve in initial classification of skin lesions, hence, can play a very important role early classification of skin lesions for people living in remote areas.

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“…The CheXNet [14] model was able to accurately identify 14 categories of abnormalities in chest X-ray images. Deep learning techniques have shown promise for automated detection and diagnosis of lung cancer [19][20][21][22], breast cancer [23,24], skin cancer [25][26][27], and other diseases. Most of these approaches use deep neural networks [28] especially convolutional neural networks [29,30].…”

Section: Introductionmentioning

confidence: 99%

Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification

Stubblefield

Hervert

Causey

et al. 2020

Preprint

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One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We evaluated ER patient classification for cardiac and infection causes with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deeplearning model. We also conducted clinical feature importance analysis and identified the most important clinical features for ER patient classification. This model can be upgraded to include a SARS-CoV-2 specific classification with COVID-19 patients data. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/.

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A comprehensive survey on image-based computer aided diagnosis systems for skin cancer

Cited by 53 publications

References 36 publications

An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Mobile-based Skin Lesions Classification Using Convolution Neural Network

Cardiac or Infectious? Transfer Learning with Chest X-Rays for ER Patient Classification

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