Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

Almeida, Marcos A. M.; Santos, I

doi:10.3390/jimaging6060051

Cited by 36 publications

(21 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A selection of 15 statistical texture features were used including first-order statistical features such as mean, standard deviation, minimum and maximum intensity, skewness, and kurtosis [ 7 ]. Also, second-order statistical methods such as gray level co-occurrence matrix (GLCM) [ 8 ], gray level run-length (GLRL), and wavelet’s transform [ 9 ] were utilized.…”

Section: Methodsmentioning

confidence: 99%

Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients

et al. 2021

View full text Add to dashboard Cite

Evaluate whether texture analysis associated with machine learning approaches could differentiate between malignant and benign lymph nodes. A total 18 patients with lung cancer were selected, with 39 lymph nodes, being 15 malignant and 24 benign. Retrospective computed tomography scans were utilized both with and without contrast medium. The great differential of this work was the use of 15 textures from mediastinal lymph nodes, with five different physicians as operators. First and second order statistical textures such as gray level run length and co-occurrence matrix were extracted and applied to three different machine learning classifiers. The best machine learning classifier demonstrated a variability of less than 5% among operators. The support vector machine (SVM) classifier presented 95% of the area under the ROC curve (AUC) and 89% of sensitivity for sequences without contrast medium. SVM classifier presented 93% of AUC and 86% of sensitivity for sequences with contrast medium. Texture analysis and machine learning may be helpful in the differentiation between malign and benign lymph nodes. This study can aid the physician in diagnosis and staging of lymph nodes and potentially reduce the number of invasive analysis to histopathological confirmation.

show abstract

Section: Methodsmentioning

confidence: 99%

Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Skin can prevent lipid deterioration in the epidermis with liquid such that the skin barrier feature can be improved. Skin diseases can arise because of fungal development over the skin, hidden bacteria, allergic reactions, microbes affecting the skin’s texture, or creating pigment [ 1 ]. Skin illnesses are chronic and occasionally may grow into malignant tissues.…”

Section: Introductionmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

482

169

View full text Add to dashboard Cite

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

show abstract

“…The ability of the skin to act as a barrier can be strengthened by employing fluids to stop the breakdown of lipids in the epidermis. Diseases of the skin can be brought on by a fungus that grows on the skin, bacteria that are not visible to the naked eye, allergic responses, bacteria that change the texture of the skin, or pigmentation [ 1 ]. Chronic skin conditions can develop into cancerous tissues on rare occasions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network

et al. 2022

View full text Add to dashboard Cite

Skin is the primary protective layer of the internal organs of the body. Nowadays, due to increasing pollution and multiple other factors, various types of skin diseases are growing globally. With variable shapes and multiple types, the classification of skin lesions is a challenging task. Motivated by this spreading deformity in society, a lightweight and efficient model is proposed for the highly accurate classification of skin lesions. Dynamic-sized kernels are used in layers to obtain the best results, resulting in very few trainable parameters. Further, both ReLU and leakyReLU activation functions are purposefully used in the proposed model. The model accurately classified all of the classes of the HAM10000 dataset. The model achieved an overall accuracy of 97.85%, which is much better than multiple state-of-the-art heavy models. Further, our work is compared with some popular state-of-the-art and recent existing models.

show abstract

Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

Cited by 36 publications

References 17 publications

Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients

Combining machine learning and texture analysis to differentiate mediastinal lymph nodes in lung cancer patients

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network

Contact Info

Product

Resources

About