Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

Ashfaq, Muniba; Minallah, Nasru; Ullah, Zahid; Ahmad, Arbab Masood; Saeed, Aamir; Hafeez, Abdul

doi:10.3390/electronics8060672

Cited by 19 publications

(8 citation statements)

References 48 publications

(67 reference statements)

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“…To completely assess the proposed approach, some reported data on the classification performance previously published on this topic are discussed. Ashfaq et al [ 33 ] associated the ABCD features with GLCM parameters and trained a Multilayer Feedforward Artificial Neural Network. However, in the reported results, there was no noticeable enhancement as compared to using ABCDE features alone.…”

Section: Resultsmentioning

confidence: 99%

“…They were correlated into two steps: in a first step, segmentation and a coarse classification were performed, and in the next step, a refinement of the coarse classification results was done by distance heat-map computation. In another paper, GLCM and color features of the lesion were combined and further used to train a Multilayer Feedforward Artificial Neural Network [ 33 ] for skin cancer detection. An accuracy of 93.7% for melanoma detection was reported for a total number of 206 images (119 melanoma and 87 non-melanoma type).…”

Section: Related Workmentioning

confidence: 99%

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Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

et al. 2021

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In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection. The reason for the choice of shape asymmetry was based on the 5–10% disagreement between dermatologists regarding the efficacy of asymmetry in the diagnosis of malignant melanoma. Asymmetry is quantified based on lesion shape (contour), moment of inertia of the lesion shape and histograms. The FFBPN has a high architecture flexibility, which indicates it as a favorable tool to avoid the over-parameterization of the ANN and, equally, to discard those redundant input datasets that usually result in poor test performance. The FFBPN was tested on four public image datasets containing melanoma, dysplastic nevus and nevus images. Experimental results on multiple benchmark data sets demonstrate that asymmetry A2 is a meaningful feature for skin lesion classification, and FFBPN with 16 neurons in the hidden layer can model the data without compromising prediction accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

et al. 2021

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“…For feature extraction, the Lesion Feature Network framework has been used. The authors in [30] connected the information about texture (features extracted from gray level co-occurrence matrix) with the information about shape and color (ABCD rule) at the entrance of an artificial neural network and obtained a detection of melanomas with an accuracy of 93.7%. Just as [18] and [28], we use information about texture, color and shape, but on separate channels.…”

Section: Related Workmentioning

confidence: 99%

Melanoma Detection Using an Objective System Based on Multiple Connected Neural Networks

Ichim

Popescu

2020

IEEE Access

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Melanoma is a common form of skin cancer that dangerously affects many people around the world. Detection of melanoma with the naked eye by dermatologists may be subject to errors. Therefore, the implementation of image processing devices equipped with artificial intelligence can act as a support for the dermatologist in examination and decision making. However, due to the various characteristics of this type of lesions and the presence of noises and artifacts in the images, it is difficult to distinguish melanomas from benign lesions. In this paper, we propose a new type of intelligent system which is based on several neural networks connected on two levels of classification. The first level contains five classifiers (subjective classifiers): the perceptron coupled with color local binary patterns, the perceptron coupled with color histograms of oriented gradients, the generative adversarial network (for segmentation) coupled with ABCD rule, the ResNet, and the AlexNet. They are chosen experimentally and consider the following features of melanomas: texture, shape, color, size, and convolutional pixel connections. At the second level (objective level), one classifier (perceptron-type) decides whether the lesion is a melanoma, based on learning-adjusted weight and the decisions at the first level. The second level is based on back-propagation perceptron that provides the final decision (melanoma or non-melanoma). The subjective and objective levels undergo two separate training phases. This approach allows an easier transition of the system from one database to another. This study shows that the use of the objective classifier brings an accuracy of 97.5% and an F1 score of 97.47%. These results are better than those of the individual classifier and those of the previous literature mentioned in References.

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“…In total, 15 images were taken for testing, 5 images each for glaucoma, DR and healthy images [19,24]. There have been several studies reported in the literature for classifying retinal images with various preprocessing methods in combination with machine learning techniques; a comprehensive review and details are provided in [25][26][27][28]. As stated in [28], Niemeijer et al used the probability of finding pixels by Gaussian filters.…”

Section: Backend Recognition Using a Neural Classifiermentioning

confidence: 99%

Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

Ghani

See

Sudhakaran³

et al. 2019

Electronics

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Diabetic retinopathy (DR) and glaucoma are common eye diseases that affect a blood vessel in the retina and are two of the leading causes of vision loss around the world. Glaucoma is a common eye condition where the optic nerve that connects the eye to the brain becomes damaged, whereas DR is a complication of diabetes caused by high blood sugar levels damaging the back of the eye. In order to produce an accurate and early diagnosis, an extremely high number of retinal images needs to be processed. Given the required computational complexity of image processing algorithms and the need for high-performance architectures, this paper proposes and demonstrates the use of fully parallel field programmable gate arrays (FPGAs) to overcome the burden of real-time computing in conventional software architectures. The experimental results achieved through software implementation were validated on an FPGA device. The results showed a remarkable improvement in terms of computational speed and power consumption. This paper presents various preprocessing methods to analyse fundus images, which can serve as a diagnostic tool for detection of glaucoma and diabetic retinopathy. In the proposed adaptive thresholding-based preprocessing method, features were selected by calculating the area of the segmented optic disk, which was further classified using a feedforward neural network (NN). The analysis was carried out using feature extraction through existing methodologies such as adaptive thresholding, histogram and wavelet transform. Results obtained through these methods were quantified to obtain optimum performance in terms of classification accuracy. The proposed hardware implementation outperforms existing methods and offers a significant improvement in terms of computational speed and power consumption.

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Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

Cited by 19 publications

References 48 publications

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

Melanoma Detection Using an Objective System Based on Multiple Connected Neural Networks

Accelerating Retinal Fundus Image Classification Using Artificial Neural Networks (ANNs) and Reconfigurable Hardware (FPGA)

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