Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach

Daghrir, Jinen; Tlig, Lotfi; Bouchouicha, Moez; Sayadi, Mounir

doi:10.1109/atsip49331.2020.9231544

Cited by 101 publications

(58 citation statements)

References 17 publications

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“…But the hand-coded feature-based approaches often show low performance in skin lesion detection because of the varying size, texture, shape, and color of melanoma moles. Daghrir, Tlig, Bouchouicha, and Sayadi (2020) proposed a method to classify melanoma lesions. After performing preprocessing on input samples, the scale-invariant feature transform (SIFT) and histogram of oriented gradients (HOG) were applied to calculate the representative set of features.…”

Section: Related Workmentioning

confidence: 99%

Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering

Nawaz

Mehmood

Nazir

et al. 2021

Microscopy Res & Technique

156

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Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively; however, the automated localization and segmentation of skin lesion at earlier stages is still a challenging task due to the low contrast between melanoma moles and skin portion and a higher level of color similarity between melanoma-affected and -nonaffected areas. In this paper, we present a fully automated method for segmenting the skin melanoma at its earliest stage by employing a deep-learningbased approach, namely faster region-based convolutional neural networks (RCNN) along with fuzzy k-means clustering (FKM). Several clinical images are utilized to test the presented method so that it may help the dermatologist in diagnosing this lifethreatening disease at its earliest stage. The presented method first preprocesses the dataset images to remove the noise and illumination problems and enhance the visual information before applying the faster-RCNN to obtain the feature vector of fixed length. After that, FKM has been employed to segment the melanoma-affected portion of skin with variable size and boundaries. The performance of the presented method is evaluated on the three standard datasets, namely ISBI-2016, ISIC-2017, and PH2, and the results show that the presented method outperforms the state-ofthe-art approaches. The presented method attains an average accuracy of 95.40, 93.1, and 95.6% on the ISIC-2016, ISIC-2017, and PH2 datasets, respectively, which is showing its robustness to skin lesion recognition and segmentation.

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Section: Related Workmentioning

confidence: 99%

Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering

Nawaz

Mehmood

Nazir

et al. 2021

Microscopy Res & Technique

156

View full text Add to dashboard Cite

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“…Researchers typically leverage segmentation models to purge background noise in the detection of apparent features from visual imagery. Researchers in [59] implemented a hybrid approach using three models for predicting lesions. The approach involves a CNN model and two conventional machine learning classification models trained using a collection of features that describe skin lesion's color, texture, and borders.…”

Section: B Deep Learning and Classical Machine Learningmentioning

confidence: 99%

“…As reported in [71], Inception-v3 achieved superior performance in comparison to ResNet-101 architecture. DenseNet121, ResNet50, and VGG11 models are used in [59] by employing ImageNet's pre-trained data. The models augmented dataset size, which led to an improved model efficiency.…”

Section: Deep Learning With Transfer Learning and Image Augmentationmentioning

confidence: 99%

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Saeed

Zeebaree

2021

JASTT

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Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

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“…Each year, the incidence rate of both melanoma and nonmelanoma continues to grow [ 2 ]. The deadliest form of skin cancer is melanoma and quickly spread to other body parts due to the malignancy of neural crest neoplasia of melanocytes [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Later, the best features are selected using Newton-Raphson (IcNR) and artificial bee colony (ABC) optimization. Daghrir et al [ 5 ] developed a hybrid approach for diagnosing suspect lesions that may be checked for melanoma skin cancer. They used a coevolutionary neural network and two classical classifiers in three different methods.…”

Section: Introductionmentioning

confidence: 99%

A Computer‐Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification

Arshad

Khan

Tariq

et al. 2021

Computational Intelligence and Neuroscience

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In the USA, each year, almost 5.4 million people are diagnosed with skin cancer. Melanoma is one of the most dangerous types of skin cancer, and its survival rate is 5%. The development of skin cancer has risen over the last couple of years. Early identification of skin cancer can help reduce the human mortality rate. Dermoscopy is a technology used for the acquisition of skin images. However, the manual inspection process consumes more time and required much cost. The recent development in the area of deep learning showed significant performance for classification tasks. In this research work, a new automated framework is proposed for multiclass skin lesion classification. The proposed framework consists of a series of steps. In the first step, augmentation is performed. For the augmentation process, three operations are performed: rotate 90, right-left flip, and up and down flip. In the second step, deep models are fine-tuned. Two models are opted, such as ResNet-50 and ResNet-101, and updated their layers. In the third step, transfer learning is applied to train both fine-tuned deep models on augmented datasets. In the succeeding stage, features are extracted and performed fusion using a modified serial-based approach. Finally, the fused vector is further enhanced by selecting the best features using the skewness-controlled SVR approach. The final selected features are classified using several machine learning algorithms and selected based on the accuracy value. In the experimental process, the augmented HAM10000 dataset is used and achieved an accuracy of 91.7%. Moreover, the performance of the augmented dataset is better as compared to the original imbalanced dataset. In addition, the proposed method is compared with some recent studies and shows improved performance.

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Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach

Cited by 101 publications

References 17 publications

Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering

Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

A Computer‐Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification

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