Skin cancer detection and classification

Dubai, Pratik; Bhatt, Sankirtan; Joglekar, Chaitanya; Patii, Sonali

doi:10.1109/iceei.2017.8312419

Cited by 40 publications

(14 citation statements)

References 10 publications

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“…The main drawback of this approach is that sometimes it may not recognize malignant moles at early stage. In Dubai et al (2017), features are extracted from the segmented images using ABCD rule and classification is performed using neural network. However, as backpropagation is used to train neural network, the convergence speed of the proposed method is slow and there are chances of getting trapped in local minima.…”

Section: Related Workmentioning

confidence: 99%

Using Transfer Learning and Hierarchical Classifier to Diagnose Melanoma From Dermoscopic Images

Bansal

Kumar

Srivastava³

et al. 2021

International Journal of Healthcare Information Systems and Informatics

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The deadliest form of skin cancer is melanoma, and if detected in time, it is curable. Detection of melanoma using biopsy is a painful and time-consuming task. Alternate means are being used by medical experts to diagnose melanoma by extracting features from skin lesion images. Medical image diagnosis requires intelligent systems. Many intelligent systems based on image processing and machine learning have been proposed by researchers in the past to detect different kinds of diseases that are successfully used by healthcare organisations worldwide. Intelligent systems to detect melanoma from skin lesion images are also evolving with the aim of improving the accuracy of melanoma detection. Feature extraction plays a critical role. In this paper, a model is proposed in which features are extracted using convolutional neural network (CNN) with transfer learning and a hierarchical classifier consisting of random forest (RF), k-nearest neighbor (KNN), and adaboost is used to detect melanoma using the extracted features. Experimental results show the effectiveness of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Using Transfer Learning and Hierarchical Classifier to Diagnose Melanoma From Dermoscopic Images

Bansal

Kumar

Srivastava³

et al. 2021

International Journal of Healthcare Information Systems and Informatics

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“…A computer-aided approach to the detection of skin cancer is proposed in [36]. Using CNNs as the future scope has been encouraged by the in Dubal et al [37] to distinguish the affected skin images without conducting feature extraction and segmentation independently [37]. A custom designed automated segmentation was used in [38] as a novel technique for applying the CNN methodology.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesion Classification Using Convolutional Neural Network With Novel Regularizer

Albahar¹

2019

IEEE Access

199

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One of the most common types of human malignancies is skin cancer, which is chiefly diagnosed visually, initiating with a clinical screening followed by dermoscopic analysis, histopathological assessment, and a biopsy. Due to the fine-grained differences in the appearance of skin lesions, automated classification is quite challenging through images. To attain highly segregated and potentially general tasks against the finely grained object categorized, deep convolutional neural networks (CNNs) are used. In this paper, we propose a new prediction model that classifies skin lesions into benign or malignant lesions based on a novel regularizer technique. Hence, this is a binary classifier that discriminates between benign or malignant lesions. The proposed model achieved an average accuracy of 97.49%, which in turns showed its superiority over other state-of-the-art methods. The performance of CNN in terms of AUC-ROC with an embedded novel regularizer is tested on multiple use cases. The area under the curve (AUC) achieved for nevus against melanoma lesion, seborrheic keratosis versus basal cell carcinoma lesion, seborrheic keratosis versus melanoma lesion, solar lentigo versus melanoma lesion is 0.77, 0.93, 0.85, and 0.86, respectively. Our results showed that the proposed learning model outperformed the existing algorithm and can be used to assist medical practitioners in classifying various skin lesions.INDEX TERMS Convolutional neural network, skin lesion, novel regularizer, AUC-ROC.

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“…In recent times, many computer aided detection (CAD) systems [14] have been proposed that help to detect different chronic diseases accurately, such as lung cancer [15], breast cancer [16], skin cancer [17], and brain cancer [18]. However, we have implemented an ensemble of deep learning and machine learning (ML) techniques for the detection of COVID-19 cases.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer

et al. 2021

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The COVID-19 virus is spreading across the world very rapidly. The World Health Organization (WHO) declared it a global pandemic on 11 March 2020. Early detection of this virus is necessary because of the unavailability of any specific drug. The researchers have developed different techniques for COVID-19 detection, but only a few of them have achieved satisfactory results. There are three ways for COVID-19 detection to date, those are real-time reverse transcription-polymerize chain reaction (RT-PCR), Computed Tomography (CT), and X-ray plays. In this work, we have proposed a less expensive computational model for automatic COVID-19 detection from Chest X-ray and CT-scan images. Our paper has a two-fold contribution. Initially, we have extracted deep features from the image dataset and then introduced a completely novel meta-heuristic feature selection approach, named Clustering-based Golden Ratio Optimizer (CGRO). The model has been implemented on three publicly available datasets, namely the COVID CT-dataset, SARS-Cov-2 dataset, and Chest X-Ray dataset, and attained state-of-the-art accuracies of 99.31%, 98.65%, and 99.44%, respectively.

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Skin cancer detection and classification

Cited by 40 publications

References 10 publications

Using Transfer Learning and Hierarchical Classifier to Diagnose Melanoma From Dermoscopic Images

Using Transfer Learning and Hierarchical Classifier to Diagnose Melanoma From Dermoscopic Images

Skin Lesion Classification Using Convolutional Neural Network With Novel Regularizer

COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer

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