An efficient 3D color-texture feature and neural network technique for melanoma detection

Warsi, Firoz; Khanam, Ruqaiya; Kamya, Suraj; Suárez-Araujo, Carmen Paz

doi:10.1016/j.imu.2019.100176

Cited by 35 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this research work, HSV colour spaces are used for image transformation. In HSV colour space, since the colours are shown within the cylindrical bounds, it becomes easier these colours to be recognized and in research areas addressing image detection (Del Alamo et al, 2013; Gao et al, 2006; Gupta & Trivedi, 2016; Singh & Gupta, 2016; Warsi et al, 2019). Our literature review has revealed that this approach has so far not been used on traffic signage datasets, though it has been used to identify skin cancer melodrama and food images (Benco et al, 2014).…”

Section: Proposed Methodology For Model Designmentioning

confidence: 99%

Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

Vashisht

Kumar

2021

Expert Systems

View full text Add to dashboard Cite

In recent past, emerging technology, such as AI has revolutionized progress and advancement of traffic management solutions in context of smart cities. This paper describes a novel approach of using 3D colour-texture based feature for image detection on public datasets of Chinese traffic sign research database (TSRD) and Mapillary image database. The implementation of 3D colour texture feature for traffic sign detection is evaluated using artificial neural network and multiple other machine learning algorithms as classifier for image detection purposes. Both datasets used in our experiments are considered among most diverse traffic signage datasets globally with annotations of almost all classes. Image datasets have been publicly made available to researchers for academic purposes. For classification of traffic sign images on Chinese TSRD and Mapillary datasets, the result outcome from backpropagation neural network (NN) classifier outperforms all other and produced best results and under multiple ML algorithms, support vector machines (SVM) cubic has best results. For classification of traffic sign images on Mapillary dataset, the result outcome from rational quadratic Gaussian process regression has best results. Also, the comparisons of results with other similar novel works have also been discussed.The proposed approach outperformed the previous experiments as observed through comparative analysis performed on both datasets. The conclusion of the research work highlights that the image detection performance is noticeably improved by using the combined 3D colour-texture feature based proposed approach.

show abstract

Section: Proposed Methodology For Model Designmentioning

confidence: 99%

Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

Vashisht

Kumar

2021

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Moreover, Jianu et al [62] use deep convolution neural networks. The main limitation of this study was to use fewer numbers of images for rare cancers like actinic keratosis, dermatofibroma and vascular lesion Whereas Warsi et al [66], use the PH2 dataset for its proposed model. For maximum accuracy, this model has to be tested on multiple datasets which contains a large number of images for training.…”

Section: A) Efficiency Calculation On Single Datasetsmentioning

confidence: 99%

“…f) PH2 Dataset [38]: This is a dermoscopic image database obtained from the Pedro Hispano Clinic, Portugal Dermatology Service. In this Review [31], [57], [62], [63], [64], [66], [70] and [82] uses PH2 for diagnosis of melanoma. PH2 contains a total of 200 dermoscopic images in which 40 were melanoma and 160 were of non-melanoma images.…”

Section: A) Isbi Challenge 2016 Dataset [5]mentioning

confidence: 99%

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

et al. 2020

View full text Add to dashboard Cite

Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.

show abstract

“…Warsi et al [29] presented a technique based on D-optimality orthogonal matching pursuit (DOOMP) to perform image enhancement, segmentation, and classification on skin lesions using fixed wavelet grid network (FWGN). The system gave accuracy results of 91.82%.…”

Section: A Semi-automatic Techniquesmentioning

confidence: 99%

“…Warsi et al designed a method termed multi-direction 3D colortexture feature (CTF) for feature extraction from dermoscopic images. They used back propagation multilayer neural network (NN) classifier for detection and classification of melanoma [29]. The shortcomings of these methods include requiring elaborate image pre-processing steps, careful initialization from a human expert and also too slow for realtime analysis and diagnosis.…”

Section: A Semi-automatic Techniquesmentioning

confidence: 99%

Deep Learning-Based System for Automatic Melanoma Detection

2020

View full text Add to dashboard Cite

Melanoma is the deadliest form of skin cancer. Distinguishing melanoma lesions from nonmelanoma lesions has however been a challenging task. Many Computer Aided Diagnosis and Detection Systems have been developed in the past for this task. They have been limited in performance due to the complex visual characteristics of the skin lesion images which consists of inhomogeneous features and fuzzy boundaries. In this paper, we propose a deep learning-based method that overcomes these limitations for automatic melanoma lesion detection and segmentation. An enhanced encoder-decoder network with encoder and decoder sub-networks connected through a series of skip pathways which brings the semantic level of the encoder feature maps closer to that of the decoder feature maps is proposed for efficient learning and feature extraction. The system employs multi-stage and multi-scale approach and utilizes softmax classifier for pixel-wise classification of melanoma lesions. We devise a new method called Lesion-classifier that performs the classification of skin lesions into melanoma and non-melanoma based on results derived from pixel-wise classification. Our experiments on two well-established public benchmark skin lesion datasets, International Symposium on Biomedical Imaging(ISBI)2017 and Hospital Pedro Hispano (PH2), demonstrate that our method is more effective than some state-of-the-art methods. We achieved accuracy and dice coefficient of 95% and 92% on ISIC 2017 dataset and accuracy and dice coefficient of 95% and 93% on PH2 datasets.

show abstract

An efficient 3D color-texture feature and neural network technique for melanoma detection

Cited by 35 publications

References 10 publications

Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

Deep Learning-Based System for Automatic Melanoma Detection

Contact Info

Product

Resources

About