BIBS, a New Descriptor for Melanoma/Non-Melanoma Discrimination

Mahdiraji, Saeid Amouzad; Baleghi, Yasser; Sakhaei, Sayed Mahmoud

doi:10.1109/icee.2018.8472701

“…While in Reference [ 15 ], authors, to properly evaluate contents of the concave contours, introduce a novel border descriptor named boundary intersection-based signature (BIBS). Shape signature is a one-dimensional illustration of shape border and cannot contribute to a proper description for concave borders that have more than one intersection points.…”

Section: Related Workmentioning

confidence: 99%

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

Manzo

¹

,

Pellino²

2020

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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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“…A shape signature is a one-dimensional representation of a shape border and cannot describe open borders with many intersecting locations. As a result, BIBS examines the boundary contents of forms, particularly those with concave contours [34]. The PCA filter feature is the mean of the input image's PCA coefficient.…”

Section: Plos Onementioning

confidence: 99%

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer’s severity using multitask feature extraction

Ahmadi,

Javaheri,

Khajavi

et al. 2024

PLoS ONE

1

0

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Alzheimer’s disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer’s disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer’s disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer’s disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.

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“…While in [15] authors, to properly evaluate contents of the concave contours, introduce a novel border descriptor named boundary intersection-based signature (BIBS). Shape signature is a one-dimensional illustration of shape border and cannot contribute to a proper description for concave borders that have more than one intersection points.…”

Section: Related Workmentioning

confidence: 99%

Bucket of deep transfer learning features and classification models for melanoma detection

Manzo¹,

Pellino²

2020

Preprint

0

View full text Add to dashboard Cite

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.

show abstract

BIBS, a New Descriptor for Melanoma/Non-Melanoma Discrimination

Cited by 10 publications

References 13 publications

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

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

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer’s severity using multitask feature extraction

Bucket of deep transfer learning features and classification models for melanoma detection

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