Rethinking Skin Lesion Segmentation in a Convolutional Classifier

Burdick, Jack; Marques, Oge; Weinthal, Janet; Furht, Borko

doi:10.1007/s10278-017-0026-y

Cited by 57 publications

(30 citation statements)

References 21 publications

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“…Unlike other applications, this one is based on clinical recognition of various oral lesions considering observation and computer vision from a smartphone. It has been evidenced that similar lesion recognition systems present a lower performance than the one obtained in this research, around 75% [14,[24][25][26][27] using image preprocessing and a higher dataset than the one used in this work. However, studies have described that good prediction performance should be greater than 90% [12,16,23,28,29].…”

Section: Discussionmentioning

confidence: 81%

See 1 more Smart Citation

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Quintero-Rojas¹,

González²

2021

Rev. Fac. Ing.

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Image recognition and processing is a suitable tool in systems using machine learning methods. The addition of smartphones as complementary tools in the health area for diagnosis is a fact nowadays due to the advantages they present. Following the trend of providing tools for diagnosis, this research aimed to develop a prototype mobile application for the identification of oral lesions, including potentially malignant lesions, based on convolutional neural networks, as early detection of indications of possible types of cancer in the oral cavity. A mobile application was developed for the Android operating system that implemented the TensorFlow library and the Mobilenet V2 convolutional neural network model. The training of the model was performed by transfer learning with a database of 500 images distributed in five classes for recognition (Leukoplakia, Herpes Simplex Virus Type 1, Aphthous stomatitis, Nicotinic stomatitis, and No lesion). The 80% of the images were used for training and 20% for validation. It was obtained that the application presented at least 80% precision in the recognition of four class. The f1-score and area under curve metrics were used to evaluate performance. The developed mobile application presented an acceptable performance with metrics higher than 75% for the recognition of three lesions, on the other hand, it yielded an unfavorable performance lower than 70% for identifying nicotinic stomatitis cases with the chosen dataset.

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Section: Discussionmentioning

confidence: 81%

“…This is the reason why this model was considered to perform the learning transfer. Lesion recognition systems using learning transfer have been described in the literature with the AlexNet [11], VGGNet [13,14], and ResNet [12,[15][16][17][18] network models, whose performance is similar to the model used for this work.…”

Section: Discussionmentioning

confidence: 99%

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Quintero-Rojas¹,

González²

2021

Rev. Fac. Ing.

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“…Our proposed segmentation pipeline method gives a less perfect segmentation of lesion than the other deep learning-based methods. However, this situation is an advantage according to a study [78] claiming that the surrounding border of the skin lesion has beneficial information in the classification of lesions. On the other hand, modified Yolov3 used in this study achieved 90% and 86% IOU rates in the detection of lesion location on the PH2 and the ISBI 2017 datasets respectively.…”

Section: Discussionmentioning

confidence: 99%

Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm

2019

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Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.

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“…In the imbalanced dataset, the number of images in the healthy, benign, malignant and eczema classes are 3014, 3014, 918 and, 1235 respectively. Different approaches are proposed in the literature (Burdick, Marques, Weinthal, & Furht, 2018;Japkowicz & Stephen, 2002) to address this issue. In this research work, a random down-sampling approach is used.…”

Section: Related Workmentioning

confidence: 99%

“…The deep learning approach is powered by the advances in computation and has been shown exceptional performance in object recognition and classification (Burdick et al, 2018;Esteva et al, 2017). Deep learning has produced results comparable to and in some cases superior to human experts.…”

Section: Deep Learning Approachmentioning

confidence: 99%

Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques

Hameed

Shabut

Ghosh

et al. 2020

Expert Systems with Applications

148

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Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the "divide and conquer" rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions.

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Rethinking Skin Lesion Segmentation in a Convolutional Classifier

Cited by 57 publications

References 21 publications

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm

Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques

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