Melanoma localization and classification through faster region-based convolutional neural network and SVM

Nawaz, Marriam; Masood, Momina; Javed, Ali; Iqbal, Javed; Nazir, Tahira; Mehmood, Awais; Ashraf, Rehan

doi:10.1007/s11042-021-11120-7

Cited by 38 publications

(19 citation statements)

References 48 publications

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“…While for the one-stage object detection methods, the position and class of RoI are determined in a single step. In the case of two-stage approaches, we have chosen the Fast-RCNN ( 53 ), Faster-RCNN ( 4 , 54 ), and Mask-RCNN ( 55 ) models, while for the other, we have taken the RetinaNet ( 56 ) and conventional CenterNet ( 21 ) models.…”

Section: Experiments and Resultsmentioning

confidence: 99%

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

Albahli

Nazir

2022

Front. Med.

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Machine learning techniques have lately attracted a lot of attention for their potential to execute expert-level clinical tasks, notably in the area of medical image analysis. Chest radiography is one of the most often utilized diagnostic imaging modalities in medical practice, and it necessitates timely coverage regarding the presence of probable abnormalities and disease diagnoses in the images. Computer-aided solutions for the identification of chest illness using chest radiography are being developed in medical imaging research. However, accurate localization and categorization of specific disorders in chest X-ray images is still a challenging problem due to the complex nature of radiographs, presence of different distortions, high inter-class similarities, and intra-class variations in abnormalities. In this work, we have presented an Artificial Intelligence (AI)-enabled fully automated approach using an end-to-end deep learning technique to improve the accuracy of thoracic illness diagnosis. We proposed AI-CenterNet CXR, a customized CenterNet model with an improved feature extraction network for the recognition of multi-label chest diseases. The enhanced backbone computes deep key points that improve the abnormality localization accuracy and, thus, overall disease classification performance. Moreover, the proposed architecture is lightweight and computationally efficient in comparison to the original CenterNet model. We have performed extensive experimentation to validate the effectiveness of the proposed technique using the National Institutes of Health (NIH) Chest X-ray dataset. Our method achieved an overall Area Under the Curve (AUC) of 0.888 and an average IOU of 0.801 to detect and classify the eight types of chest abnormalities. Both the qualitative and quantitative findings reveal that the suggested approach outperforms the existing methods, indicating the efficacy of our approach.

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Section: Experiments and Resultsmentioning

confidence: 99%

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

Albahli

Nazir

2022

Front. Med.

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“…At the start, a keypoint extractor unit is used which extracts the reliable feature vector that is employed to estimate the heatmaps (Hms), embeddings, offset, and class (C). The Hms is concerned to give the approximation if a specific location in a U-Net + SVM Categorization ISIC-2016 76% Daghrir et al [23] SIFT + SVM and KNN Categorization ISIC-2017 88.40% Bama et al [24] GMM model Segmentation PH2 86.83% Hu et al [25] SIFT + SVM Categorization PH2 82% Durgarao et al [44] LVP, and LBP + C-means Segmentation PH2 79.44% DL techniques Ameri et al [26] AlexNet Categorization HAM10000 84% Acosta et al [27] ResNet-152 Categorization ISIC-2017 90.40% Zhang et al [28] VGG-16 Categorization ISIC-2017 92.72% Shan et al [29] FC-DPN Segmentation ISIC-2017 95.14% Bi et al [30] Res-FCN Segmentation ISIC-2016 95.78% Adegun et al [31] Encoder-decoder Categorization ISIC-2017 95% Nawaz et al [32] Faster-RCNN + FKM Segmentation PH2 95.6% Nawaz et al [35] Faster-RCNN + SVM Categorization ISIC-2016 89.10% Banerjee et al [36] YOLO + L-type fuzzy clustering Segmentation ISIC-2017 97.33% Iqbal et al [37] CNN Categorization ISIC-2019 88.75% Khan et al [38] Mask-RCNN, DenseNet201 + SVM Segmentation ISIC-2016 93.6% Mohakud et al [39] Encoder-decoder Segmentation ISIC-2016 98.32% Abdar et al [40] Bayesian model Categorization Kaggle skin cancer dataset 88.95% Pacheco et al [41] Metadata and block-based method Categorization ISIC-2019 74.90% Wang et al [42] U-Net Segmentation ISIC-2017 94.67% Zhao et al [43] U-Net++ Segmentation ISIC-2018 95.30% Ali et al [46] DCNN Categorization HAM10000 91.93% 5 Computational and Mathematical Methods in Medicine sample is a TL/BR corner associated with a particular category [51], while the embeddings are used to discriminate the detected pairs of corners and offsets to fine-tune the box position. The corners with high-scored TL and BR coordinates are employed to regulate the exact position of the box, whereas the associated category for each detected diseased region is specified by using the embedding distances on the computed feature vector.…”

Section: Methodsmentioning

confidence: 99%

“…Nawaz et al [35] presented another framework for the automated identification and classification of the skin model via employing the Faster-RCNN model together with the SVM approach. The work elaborated in [35] is robust to melanoma classification because of its empowerment to tackle the model overfitting data. However, the works discussed in [32,35] have highly relied on the selection of hyperparameters in the model training phase.…”

Section: Related Workmentioning

confidence: 99%

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MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$ -Means Clustering

Nawaz

Nazir

Khan

et al. 2022

Computational and Mathematical Methods in Medicine

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Melanoma is a dangerous form of skin cancer that results in the demise of patients at the developed stage. Researchers have attempted to develop automated systems for the timely recognition of this deadly disease. However, reliable and precise identification of melanoma moles is a tedious and complex activity as there exist huge differences in the mass, structure, and color of the skin lesions. Additionally, the incidence of noise, blurring, and chrominance changes in the suspected images further enhance the complexity of the detection procedure. In the proposed work, we try to overcome the limitations of the existing work by presenting a deep learning (DL) model. Descriptively, after accomplishing the preprocessing task, we have utilized an object detection approach named CornerNet model to detect melanoma lesions. Then the localized moles are passed as input to the fuzzy K -means (FLM) clustering approach to perform the segmentation task. To assess the segmentation power of the proposed approach, two standard databases named ISIC-2017 and ISIC-2018 are employed. Extensive experimentation has been conducted to demonstrate the robustness of the proposed approach through both numeric and pictorial results. The proposed approach is capable of detecting and segmenting the moles of arbitrary shapes and orientations. Furthermore, the presented work can tackle the presence of noise, blurring, and brightness variations as well. We have attained the segmentation accuracy values of 99.32% and 99.63% over the ISIC-2017 and ISIC-2018 databases correspondingly which clearly depicts the effectiveness of our model for the melanoma mole segmentation.

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“…Data augmentation is a way to expand the training dataset by transforming input images without having to collect new datasets for model training, thus avoiding the overfitting issue that might occur during the training process when a small amount of training data is used. These papers use data augmentation for performance enhancement: [25], [26], [34]- [36], [40], [41], [46], [46], [47], [49], [50], [52], [56], [58], [60], [60], [61], [64], [67], [68], [85], [88], [90], [91], [98], [105], [107], [109], [115], [116], [122], [124]- [127], [130], [151], [152], [154], [155], [158], [159], [162]- [166], [168]- [170], [180]- [186]. The literature includes several w...…”

Section: Data Augmentationmentioning

confidence: 99%

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

et al. 2022

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Skin cancer is one of the most threatening cancers, which spreads to the other parts of the body if not caught and treated early. During the last few years, the integration of deep learning into skin cancer has been a milestone in health care, and dermoscopic images are right at the center of this revolution. This review study focuses on the state-of-the-art automatic diagnosis of skin cancer from dermoscopic images based on deep learning. This work thoroughly explores the existing deep learning and its application in diagnosing dermoscopic images. This study aims to present and summarize the latest methodology in melanoma classification and the techniques to improve this. We discuss advancements in deep learningbased solutions to diagnose skin cancer, along with some challenges and future opportunities to strengthen these automatic systems to support dermatologists and enhance their ability to diagnose skin cancer. INDEX TERMSSkin cancer, Dermoscopy images, Deep learning, Classification, Literature review.FIGURE 1: Global heat map showing estimated agestandardized incidence rates, in 2020, of melanoma of the skin in all sexes, all ages. The map shows melanoma incidence in all parts of the world, except Greenland in the Arctic Circle. The regions most affected by skin melanoma globally are Europe, the United States, Canada, and Australia [2].microscopy-based tool to improve non-invasive diagnostic discrimination of skin lesions based on color and structure analysis [5]. This paper focuses on dermoscopy images. Because dermoscopic structures have direct histopathologic correlates, dermoscopic images help the dermatologist select management and treatment options for particular types of skin cancers [6]. In addition, dermoscopy can be useful for

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Melanoma localization and classification through faster region-based convolutional neural network and SVM

Cited by 38 publications

References 48 publications

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$ -Means Clustering

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

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Melanoma localization and classification through faster region-based convolutional neural network and SVM

Cited by 38 publications

References 48 publications

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease

MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy K -Means Clustering

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

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MSeg-Net: A Melanoma Mole Segmentation Network Using CornerNet and Fuzzy $K$ -Means Clustering