Classification of skin cancer based on fluorescence lifetime imaging and machine learning

Yang, Qianqian; Qi, Meijie; Wu, Zhaoqing; Liu, Lixin; Gao, Peng; Qu, Junle

doi:10.1117/12.2573851

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…To the best of our knowledge, only one published work has reported the use of machine learning models based on autofluorescence lifetime imaging features for the classification of pigmented skin lesions. 12 In that study, however, only skin melanoma lesions were imaged, and the classification task was restricted to discriminate early-stage from advanced-stage skin melanoma. In contrast, a more comprehensive set of pigmented skin lesions were imaged in this work (two benign and three malignant lesion categories).…”

Section: Discussionmentioning

confidence: 99%

Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning

et al. 2022

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. Significance: Accurate early diagnosis of malignant skin lesions is critical in providing adequate and timely treatment; unfortunately, initial clinical evaluation of similar-looking benign and malignant skin lesions can result in missed diagnosis of malignant lesions and unnecessary biopsy of benign ones. Aim: To develop and validate a label-free and objective image-guided strategy for the clinical evaluation of suspicious pigmented skin lesions based on multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy. Approach: We tested the hypothesis that maFLIM-derived autofluorescence global features can be used in machine-learning (ML) models to discriminate malignant from benign pigmented skin lesions. Clinical widefield maFLIM dermoscopy imaging of 41 benign and 19 malignant pigmented skin lesions from 30 patients were acquired prior to tissue biopsy sampling. Three different pools of global image-level maFLIM features were extracted: multispectral intensity, time-domain biexponential, and frequency-domain phasor features. The classification potential of each feature pool to discriminate benign versus malignant pigmented skin lesions was evaluated by training quadratic discriminant analysis (QDA) classification models and applying a leave-one-patient-out cross-validation strategy. Results: Classification performance estimates obtained after unbiased feature selection were as follows: 68% sensitivity and 80% specificity with the phasor feature pool, 84% sensitivity, and 71% specificity with the biexponential feature pool, and 84% sensitivity and 32% specificity with the intensity feature pool. Ensemble combinations of QDA models trained with phasor and biexponential features yielded sensitivity of 84% and specificity of 90%, outperforming all other models considered. Conclusions: Simple classification ML models based on time-resolved (biexponential and phasor) autofluorescence global features extracted from maFLIM dermoscopy images have the potential to provide objective discrimination of malignant from benign pigmented lesions. ML-assisted maFLIM dermoscopy could potentially assist with the clinical evaluation of suspicious lesions and the identification of those patients benefiting the most from biopsy examination.

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Section: Discussionmentioning

confidence: 99%

Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning

et al. 2022

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“…A study proposed CNN methods to detect segmented skin lesions using the ISIC 2016, 2017, and 2018 datasets. In the article, accuracy improvement reached 0.78% of 81.60% and 81.57% [20]. Another study presented detection of other skin lesions using CNN with a novel regularizer.…”

Section: Related Workmentioning

confidence: 94%

DeepSkin: Robust Skin Cancer Classification Using Convolutional Neural Network Algorithm

2022

ijicom

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Classification of skin cancer is a growing research topic with significant challenges in the image processing. Learning algorithms for classifying a kind of skin cancer have been presented in recent articles to accelerate the diagnosis process with a rapid and accurate diagnosis. However, effective detection of skin cancer requires extensive graphical data. Inspired by deep learning successful results in computer vision, A Convolutional Neural Network (CNN) is proposed in this study to build a skin cancer classification model. We conduct this experiment by collecting massive skin cancer datasets, conducting pre-processing, training models, and evaluating the performance. Based on the experiment result, the benign and malignant classification model can obtain a good accuracy with a slight loss. Therefore, the results obtained reached an accuracy of 54%.

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“…However, testing methods are not meant to be compared and should not be considered hyperparameters. Yang et al [ 140 ] used bootstrapping, hold-out and K-fold CV (K-CV) and compared the accuracy obtained. Besides wrongfully comparing the methods, the difference in the value of their metrics is very wide, hinting at some issue with over-fitting or in the distribution of their test set.…”

Section: Fluorescence Lifetime Image Analysesmentioning

confidence: 99%

Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

Gouzou,

Taimori,

Haloubi

et al. 2023

Methods Appl. Fluoresc.

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Many medical imaging modalities have benefited from recent advances in Machine Learning (ML), specifically in deep learning, such as neural networks.Computers can be trained to investigate and enhance medical imaging methods without using valuable human resources.In recent years, Fluorescence Lifetime Imaging (FLIm) has received increasing attention from the ML community. FLIm goes beyond conventional spectral imaging, providing additional lifetime information, and could lead to optical histopathology supporting real-time diagnostics.However, most current studies do not use the full potential of machine/deep learning models. As a developing image modality, FLIm data are not easily obtainable, which, coupled with an absence of standardisation, is pushing back the research to develop models which could advance automated diagnosis and help promote FLIm.In this paper, we describe recent developments that improve FLIm image quality, specifically time-domain systems, and we summarise sensing, signal-to-noise analysis and the advances in registration and low-level tracking. We review the two main applications of ML for FLIm: lifetime estimation and image analysis through classification and segmentation. We suggest a course of action to improve the quality of ML studies applied to FLIm. Our final goal is to promote FLIm and attract more ML practitioners to explore the potential of lifetime imaging.

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Classification of skin cancer based on fluorescence lifetime imaging and machine learning

Cited by 4 publications

References 9 publications

Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning

Discrimination of cancerous from benign pigmented skin lesions based on multispectral autofluorescence lifetime imaging dermoscopy and machine learning

DeepSkin: Robust Skin Cancer Classification Using Convolutional Neural Network Algorithm

Applications of Machine Learning in time-domain Fluorescence Lifetime Imaging: a Review

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