Classification of skin cancer based on hyperspectral microscopic imaging and machine learning

Qi, Meijie; Liu, Yujie; Li, Ranru; Liu, Lixin; Zhang, Zhoufeng

doi:10.1117/12.2666425

Cited by 2 publications

(1 citation statement)

References 9 publications

(9 reference statements)

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“…Existing works in the literature have focused on the classification of HSI skin cancer images by adopting machine-learning (ML) and deep-learning (DL) methods [ 11 , 16 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Acceleration of Hyperspectral Skin Cancer Image Classification through Parallel Machine-Learning Methods

Petracchi,

Torti,

Marenzi

et al. 2024

Sensors

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Hyperspectral imaging (HSI) has become a very compelling technique in different scientific areas; indeed, many researchers use it in the fields of remote sensing, agriculture, forensics, and medicine. In the latter, HSI plays a crucial role as a diagnostic support and for surgery guidance. However, the computational effort in elaborating hyperspectral data is not trivial. Furthermore, the demand for detecting diseases in a short time is undeniable. In this paper, we take up this challenge by parallelizing three machine-learning methods among those that are the most intensively used: Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) algorithms using the Compute Unified Device Architecture (CUDA) to accelerate the classification of hyperspectral skin cancer images. They all showed a good performance in HS image classification, in particular when the size of the dataset is limited, as demonstrated in the literature. We illustrate the parallelization techniques adopted for each approach, highlighting the suitability of Graphical Processing Units (GPUs) to this aim. Experimental results show that parallel SVM and XGB algorithms significantly improve the classification times in comparison with their serial counterparts.

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Section: Introductionmentioning

confidence: 99%

Acceleration of Hyperspectral Skin Cancer Image Classification through Parallel Machine-Learning Methods

Petracchi,

Torti,

Marenzi

et al. 2024

Sensors

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DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework

Veeramani,

Jayaraman,

Krishankumar

et al. 2024

Sci Rep

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Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel ‘F’ Flag feature for early detection. This novel ‘F’ indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma. The article proposes an architecture that is built in a Double Decker Convolutional Neural Network called DDCNN future fusion. The network's deck one, known as a Convolutional Neural Network (CNN), finds difficult-to-classify hairy images using a confidence factor termed the intra-class variance score. These hirsute image samples are combined to form a Baseline Separated Channel (BSC). By eliminating hair and using data augmentation techniques, the BSC is ready for analysis. The network's second deck trains the pre-processed BSC and generates bottleneck features. The bottleneck features are merged with features generated from the ABCDE clinical bio indicators to promote classification accuracy. Different types of classifiers are fed to the resulting hybrid fused features with the novel 'F' Flag feature. The proposed system was trained using the ISIC 2019 and ISIC 2020 datasets to assess its performance. The empirical findings expose that the DDCNN feature fusion strategy for exposing malignant melanoma achieved a specificity of 98.4%, accuracy of 93.75%, precision of 98.56%, and Area Under Curve (AUC) value of 0.98. This study proposes a novel approach that can accurately identify and diagnose fatal skin cancer and outperform other state-of-the-art techniques, which is attributed to the DDCNN ‘F’ Feature fusion framework. Also, this research ascertained improvements in several classifiers when utilising the ‘F’ indicator, resulting in the highest specificity of + 7.34%.

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Classification of skin cancer based on hyperspectral microscopic imaging and machine learning

Cited by 2 publications

References 9 publications

Acceleration of Hyperspectral Skin Cancer Image Classification through Parallel Machine-Learning Methods

Acceleration of Hyperspectral Skin Cancer Image Classification through Parallel Machine-Learning Methods

DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework

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