Actinic Keratosis and Squamous Cell Carcinoma

Lalji, Aliya

doi:10.15226/2378-1726/1/1/00102

CRDOA

2014

DOI: 10.15226/2378-1726/1/1/00102

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Actinic Keratosis and Squamous Cell Carcinoma

Aliya Lalji¹

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2014

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Cited by 3 publications

(1 citation statement)

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“…The literature review by Lalji et al [3] tried to assess the differences between actinic keratosis and squamous cell carcinoma (SCC). They found it is difficult to distinguish between these two conditions in Pubmed, Cochrane and Medline databases.…”

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confidence: 99%

A Review on Articles in the Current Issue

Chen¹

2014

CRDOA

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It is a great pleasure to introduce a new type of dermatological journal here. Clinical Research in Dermatology: Open Access (CRDOA) is a new international journal aimed to provide interesting and intriguing research in dermatology through free online open access.In this inaugural issue, we have some interesting scientific findings from Turkey, Australia, America, Taiwan, and Brazil. Cutaneous findings always provide an insight to the underlying systemic disease during in-patient dermatological consultation. Demir et al. [1] found most consultations were requested from Internal medicine and the patients over the age of 60 years. Drug eruption was the most frequent diagnosis, probably reflecting the in-patients taking lots of drugs when hospitalized. Over half of dermatologic diagnoses were incorrect by the nondermatologists, revealing the importance of dermatology branch in the hospital setting.

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confidence: 99%

A Review on Articles in the Current Issue

Chen¹

2014

CRDOA

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Role of asprosin and meteorin-like peptide in progression of actinic keratosis to squamous cell carcinoma

Inan Yuksel,

Cicek,

Demir

et al. 2024

Biotechnic & Histochemistry

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A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

Manzoor¹,

Majeed²,

Siddique³

et al. 2022

Computers, Materials &Amp; Continua

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Skin diseases effectively influence all parts of life. Early and accurate detection of skin cancer is necessary to avoid significant loss. The manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color levels. Therefore, an automated system to identify these skin diseases is required. Few studies on skin disease classification using different techniques have been found. However, previous techniques failed to identify multi-class skin disease images due to their similar appearance. In the proposed study, a computer-aided framework for automatic skin disease detection is presented. In the proposed research, we collected and normalized the datasets from two databases (ISIC archive, Mendeley) based on six Basal Cell Carcinoma (BCC), Actinic Keratosis (AK), Seborrheic Keratosis (SK), Nevus (N), Squamous Cell Carcinoma (SCC), and Melanoma (M) common skin diseases. Besides, segmentation is performed using deep Convolutional Neural Networks (CNN). Furthermore, three types of features are extracted from segmented skin lesions: ABCD rule, GLCM, and in-depth features. AlexNet transfer learning is used for deep feature extraction, while a support vector machine (SVM) is used for classification. Experimental results show that SVM outperformed other studies in terms of accuracy, as AK disease achieved 100% accuracy, BCC 92.7%, M 95.1%, N 97.8%, SK 93.1%, SCC 91.4% with a global accuracy of 95.4%.

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Actinic Keratosis and Squamous Cell Carcinoma

Cited by 3 publications

References 12 publications

A Review on Articles in the Current Issue

A Review on Articles in the Current Issue

Role of asprosin and meteorin-like peptide in progression of actinic keratosis to squamous cell carcinoma

A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

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