Melanoma detection using color and texture features in computer vision systems

Fuduli, Antonio; Veltri, Pierangelo; Vocaturo, Eugenio; Zumpano, Ester

doi:10.25046/aj040502

Cited by 29 publications

(4 citation statements)

References 35 publications

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“…For example, a combination of nomogram and machine learning prediction models can be considered for analysis. Recently, multiple instance learning has demonstrated its superiority in various applications including tumor imaging analysis [ 54 , 55 , 56 , 57 ]. The deployment of the multiple instances learning method may significantly improve prognosis prediction for cancer patients.…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

et al. 2021

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Background and Objective: Primary lung cancer is a lethal and rapidly-developing cancer type and is one of the most leading causes of cancer deaths. Materials and Methods: Statistical methods such as Cox regression are usually used to detect the prognosis factors of a disease. This study investigated survival prediction using machine learning algorithms. The clinical data of 28,458 patients with primary lung cancers were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Results: This study indicated that the survival rate of women with primary lung cancer was often higher than that of men (p < 0.001). Seven popular machine learning algorithms were utilized to evaluate one-year, three-year, and five-year survival prediction The two classifiers extreme gradient boosting (XGB) and logistic regression (LR) achieved the best prediction accuracies. The importance variable of the trained XGB models suggested that surgical removal (feature “Surgery”) made the largest contribution to the one-year survival prediction models, while the metastatic status (feature “N” stage) of the regional lymph nodes was the most important contributor to three-year and five-year survival prediction. The female patients’ three-year prognosis model achieved a prediction accuracy of 0.8297 on the independent future samples, while the male model only achieved the accuracy 0.7329. Conclusions: This data suggested that male patients may have more complicated factors in lung cancer than females, and it is necessary to develop gender-specific diagnosis and prognosis models.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

et al. 2021

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“…Specifically, authors introduce an MIL approach that adopts spherical separation surfaces. Finally, in Reference [ 28 ], a preliminary comparison between two different approaches, SVM and MIL, is proposed, focusing on the key role played by the feature selection (color and texture). In particular, the authors are inspired by the good results obtained applying MIL techniques for classifying some medical dermoscopic images.…”

Section: Related Workmentioning

confidence: 99%

Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

Manzo

Pellino²

2020

J. Imaging

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Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.

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“…De acuerdo con Fuduli et al [4], existen varias propuestas de sistemas de visión arti-ficial para la detección temprana de los melanomas que se caracterizan por seguir pasos generales básicos que incluyen la adquisición de imágenes, el pre procesamiento, la segmentación, la extracción, la selección de características y la clasificación final. Dado que el resultado de cada paso es la entrada del siguiente, todas las etapas juegan un papel clave para lograr un diagnóstico correcto.…”

Section: Proposal Of a Machine Learning Application With Artificial V...unclassified

Propuesta de una aplicación de aprendizaje automático con visión artificial como herramienta de apoyo para la detección de melanomas benignos y malignos.

Quintanilla,

Tamayo,

Suntaxi

et al. 2022

GEEKS

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El cáncer de piel es una de las enfermedades que más muertes causa mundialmente, siendo especialmente conocida por la agresividad con la que esta enfermedad se propaga. Sin embargo, la tasa de supervivencia es alta si los melanomas son detectados en etapas iniciales, siendo diagnosticados inicialmente por su tamaño, forma y coloración antes de continuar con procesos invasivos. En el presente paper se muestra una propuesta de aplicación como una herramienta aplicando técnicas de machine learning y procesamiento de imágenes que sea capaz de facilitar el diagnóstico de los melanomas categorizándolos por las características presentadas por la lesión analizada, con el propósito de ayudar al especialista en el diagnóstico del tratamiento que el paciente con esta lesión cutánea deba tomar.

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Melanoma detection using color and texture features in computer vision systems

Cited by 29 publications

References 35 publications

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

Propuesta de una aplicación de aprendizaje automático con visión artificial como herramienta de apoyo para la detección de melanomas benignos y malignos.

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