A Convolutional Neural Network for Lentigo Diagnosis

Zorgui, Sana; Chaabene, Siwar; Bouaziz, Bassem; Batatia, Hadj; Chaâri, Lotfi

doi:10.1007/978-3-030-51517-1_8

Cited by 14 publications

(15 citation statements)

References 20 publications

(26 reference statements)

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“…In addition, deep learning (DL) researches [ 31 , 32 ] have recently shown notable progress in biomedical signal analysis especially classification-based anomaly detection. However, DL [ 33 ] is now the fastest sub-field of ML technology [ 34 ] based on the artificial neural networks (ANNs) [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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“…Machine learning approaches specifically to the classification of RCM images of skin lesions have focused until now on BCC and melanoma diagnosis (Campanella et al, 2021;Wodzinski et al, 2020), lentigos (Halimi et al, 2017a(Halimi et al, , 2017bZorgui et al, 2020), and congenital pigmented macules (Soenen et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Training data sets for these previous studies have included single images sometimes pre-selected in the vicinity of the DEJ (Soenen et al, 2021), RCM mosaics (Wodzinski et al, 2020) or 3D reconstructions (Zorgui et al, 2020). In contrast, we utilised a projection approach to project 3D and volumetric image data into a 2D representation of that volume as it offered some specific benefits.…”

Section: Discussionmentioning

confidence: 99%

Computer-aided diagnosis of reflectance confocal images to differentiate between lentigo maligna (LM) and atypical intraepidermal melanocytic proliferation (AIMP)

Mandal

Priyam

Chan

et al. 2022

Preprint

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Lentigo maligna (LM), a form of melanoma in situ that predominantly affects sun-exposed areas such as the face, has an ill-defined clinical border and has a high rate of recurrence. Atypical Intraepidermal Melanocytic Proliferation (AIMP) is a term used to describe the melanocytic proliferation of an uncertain malignant potential. Clinically and histologically, AIMP can be difficult to distinguish from LM, and indeed AIMP may in some cases progress to LM. Reflectance Confocal Microscopy (RCM) is often used to investigate these lesions non-invasively, however, RCM is often not readily available nor is the associated expertise for RCM image interpretation. Here, we demonstrate machine learning architectures that can correctly classify lesions between LM and AIMP on stacks of RCM images. Overall, our methods showcase the potential for computer-aided diagnosis in dermatology, which in conjunction with the remote acquisition, can expand the range of diagnostic tools in the community.

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“…Sensitivity and specificity were 81.4% and 83.3% respectively at depths from 50 to 60 µm, approximately the depth of the DEJ [64]. Another group used a convolutional neural network to classify solar lentigines and achieved 98% accuracy, 96% sensitivity, and 100% specificity [65].…”

Section: Analyzing Pigmented Lesionsmentioning

confidence: 99%

“…Texton-based algorithm [43] Texton-based algorithm [47][48][49][50][51][52] Computation of DEJ roughness [61] Transformation analysis [67] Logistic regression [24] Conditional random fields [54,55] Classification and Regression Trees [58,62] Support vector machine [41,68] Support vector machine [25] Spatial Poisson process [53] Convolutional neural network [60,62,65] Random Forest Classifier [69] Hybrid deep learning [26] Support vector machine [47][48][49][50][51][52] Support vector machine [59,62] 3D reconstruction [70] Convolutional neural network [12,44] Recurrent neural network [56,57] Recurrent neural network plus Convolutional neural network [56,57] Tree-of-shapes [71] Wavelet transform [46] Bayesian Model [63] Wavelet decomposition with support vector machine [64] articles that analyze automatic image processing of RCM images. Technical details of each algorithm were not analyzed in detail, as the goal of this review was to give clinicians an idea of how RCM image interpretation could be applied to the clinical setting using developed software mechanisms.…”

Section: Quantification Of Photoagingmentioning

confidence: 99%

A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology

et al. 2021

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Background and Objectives Non‐invasive optical imaging has the potential to provide a diagnosis without the need for biopsy. One such technology is reflectance confocal microscopy (RCM), which uses low power, near‐infrared laser light to enable real‐time in vivo visualization of superficial human skin from the epidermis down to the papillary dermis. Although RCM has great potential as a diagnostic tool, there is a need for the development of reliable image analysis programs, as acquired grayscale images can be difficult and time‐consuming to visually assess. The purpose of this review is to provide a clinical perspective on the current state of artificial intelligence (AI) for the analysis and diagnostic utility of RCM imaging. Study Design/Materials and Methods A systematic PubMed search was conducted with additional relevant literature obtained from reference lists. Results Algorithms used for skin stratification, classification of pigmented lesions, and the quantification of photoaging were reviewed. Image segmentation, statistical methods, and machine learning techniques are among the most common methods used to analyze RCM image stacks. The poor visual contrast within RCM images and difficulty navigating image stacks were mediated by machine learning algorithms, which allowed the identification of specific skin layers. Conclusions AI analysis of RCM images has the potential to increase the clinical utility of this emerging technology. A number of different techniques have been utilized but further refinements are necessary to allow consistent accurate assessments for diagnosis. The automated detection of skin cancers requires more development, but future applications are truly boundless, and it is compelling to envision the role that AI will have in the practice of dermatology. Lasers Surg. Med. © 2020 Wiley Periodicals LLC

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A Convolutional Neural Network for Lentigo Diagnosis

Cited by 14 publications

References 20 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Computer-aided diagnosis of reflectance confocal images to differentiate between lentigo maligna (LM) and atypical intraepidermal melanocytic proliferation (AIMP)

A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology

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