Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN

Mohakud, Rasmiranjan; Dash, Rajashree

doi:10.1016/j.jksuci.2021.12.018

Cited by 35 publications

(26 citation statements)

References 29 publications

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“…They achieved the highest performance values of 80.46%, 78.56%, and 74.15% for 80 × 80, 64 × 64, and 32 × 32 pixels, respectively. Mohakud et al [ 9 ] has proposed an encoder decoder network for segmentation of image. The authors obtained the value of the Jaccard coefficient as 96.41% and 86.85% respectively, and the Dice coefficient as 98.48% and 87.23%, accuracy as 98.32% and 95.25% respectively for ISIC 2016 and ISIC 2017 dataset.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

et al. 2022

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Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that can deliver a fast judgment and considerably reduce mistakes in diagnosis. In this work, a deep learning-based model has been designed for the identification of skin cancer at benign and malignant stages using the concept of transfer learning approach. For this, a pre-trained VGG16 model is improved by adding one flatten layer, two dense layers with activation function (LeakyReLU) and another dense layer with activation function (sigmoid) to enhance the accuracy of this model. This proposed model is evaluated on a dataset obtained from Kaggle. The techniques of data augmentation are applied in order to enhance the random-ness among the input dataset for model stability. The proposed model has been validated by considering several useful hyper parameters such as different batch sizes of 8, 16, 32, 64, and 128; different epochs and optimizers. The proposed model is working best with an overall accuracy of 89.09% on 128 batch size with the Adam optimizer and 10 epochs and outperforms state-of-the-art techniques. This model will help dermatologists in the early diagnosis of skin cancers.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

et al. 2022

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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%

“…The method elaborated in [38] attained the clustering and categorization results of 93.60% and 96.30%, correspondingly, however, at the expense of increased model complexity. Many other researchers have attempted to classify and segment the skin cancer moles [39][40][41][42][43][44][45]; however, there is a demand for performance enhancement. Besides, the expense of processing power for such methods is a substantial barrier in medical applications.…”

Section: Related Workmentioning

confidence: 99%

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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“…Firstly, we compare the optimization algorithms. The results of OBLAOA are compared with the following algorithms: Arithmetic Optimization Algorithm (AOA), Whale Optimization Algorithm (WOA) [45], Salp Swarm Algorithm (SSA) [46], Weighted Salp Swarm Algorithms (WSSA) [47], Exponential Neighborhood Grey Wolf Optimization (ENGWO) [48], developed Arithmetic Optimization Algorithm (dAOA) [49] and improved arithmetic optimization algorithm (IAOA) [50]. Secondly, we compare our OBLAOA-DBSCAN algorithm with five classical clustering algorithms, namely K-means [51], Spectral [52], OPTICS [53], clustering by fast search and find of density peaks (DPC) [54] and the original DBSCAN.…”

Section: Experiments Settingsmentioning

confidence: 99%

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

Yang

Chen

et al. 2022

J Supercomput

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As unsupervised learning algorithm, clustering algorithm is widely used in data processing field. Density-based spatial clustering of applications with noise algorithm (DBSCAN), as a common unsupervised learning algorithm, can achieve clusters via finding high-density areas separated by low-density areas based on cluster density. Different from other clustering methods, DBSCAN can work well for any shape clusters in the spatial database and can effectively cluster exceptional data. However, in the employment of DBSCAN, the parameters, EPS and MinPts, need to be preset for different clustering object, which greatly influences the performance of the DBSCAN. To achieve automatic optimization of parameters and improve the performance of DBSCAN, we proposed an improved DBSCAN optimized by arithmetic optimization algorithm (AOA) with opposition-based learning (OBL) named OBLAOA-DBSCAN. In details, the reverse search capability of OBL is added to AOA for obtaining proper parameters for DBSCAN, to achieve adaptive parameter optimization. In addition, our proposed OBLAOA optimizer is compared with standard AOA and several latest meta heuristic algorithms based on 8 benchmark functions from CEC2021, which validates the exploration improvement of OBL. To validate the clustering performance of the OBLAOA-DBSCAN, 5 classical clustering methods with 10 real datasets are chosen as the compare models according to the computational cost and accuracy. Based on the experimental results, we can obtain two conclusions: (1) the proposed OBLAOA-DBSCAN can provide highly accurately clusters more efficiently; and (2) the OBLAOA can significantly improve the exploration ability, which can provide better optimal parameters.

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Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN

Cited by 35 publications

References 29 publications

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

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

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

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Skin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN

Cited by 35 publications

References 29 publications

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

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

An efficient DBSCAN optimized by arithmetic optimization algorithm with opposition-based learning

Contact Info

Product

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