An automatic skin lesion segmentation system with hybrid FCN-ResAlexNet

Barin, Sezin; Güraksın, Gür Emre

doi:10.1016/j.jestch.2022.101174

Cited by 12 publications

(4 citation statements)

References 57 publications

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“…For the ISIC 2017 dataset, the proposed algorithm is compared with several models including deep convolutional deconvolution neural network (CDNN) [42], fully convolutional network (FCN) [43], a fully convolutional-residual network (FCRN) [44], segmentation by assembling crowdsourced results of ISIC 2017 challenge [45], transfer learning with pre-trained VGG16 or ResNet50 [46], a fully convolutional network (FCN) architecture with ResNet18 and AlexNet in encoder and three deconvolution layers in decoder part [47], and FrCN model for simultaneous segmentation and classification [48]. Among these, the accuracy performance of Hasan et al [39] is the second-best in the table with a score of 95.3%, while our proposed method achieves an accuracy score of 96.17%.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

Fuzzy Logic with Deep Learning for Detection of Skin Cancer

2023

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Melanoma is the deadliest type of cancerous cell, which is developed when melanocytes, melanin producing cell, starts its uncontrolled growth. If not detected and cured in its situ, it might decrease the chances of survival of patients. The diagnosis of a melanoma lesion is still a challenging task due to its visual similarities with benign lesions. In this paper, a fuzzy logic-based image segmentation along with a modified deep learning model is proposed for skin cancer detection. The highlight of the paper is its dermoscopic image enhancement using pre-processing techniques, infusion of mathematical logics, standard deviation methods, and the L-R fuzzy defuzzification method to enhance the results of segmentation. These pre-processing steps are developed to improve the visibility of lesion by removing artefacts such as hair follicles, dermoscopic scales, etc. Thereafter, the image is enhanced by histogram equalization method, and it is segmented by proposed method prior to performing the detection phase. The modified model employs a deep neural network algorithm, You Look Only Once (YOLO), which is established on the application of Deep convolutional neural network (DCNN) for detection of melanoma lesion from digital and dermoscopic lesion images. The YOLO model is composed of a series of DCNN layers we have added more depth by adding convolutional layer and residual connections. Moreover, we have introduced feature concatenation at different layers which combines multi-scale features. Our experimental results confirm that YOLO provides a better accuracy score and is faster than most of the pre-existing classifiers. The classifier is trained with 2000 and 8695 dermoscopic images from ISIC 2017 and ISIC 2018 datasets, whereas PH2 datasets along with both the previously mentioned datasets are used for testing the proposed algorithm.

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Section: Analysis Of Resultsmentioning

confidence: 99%

Fuzzy Logic with Deep Learning for Detection of Skin Cancer

2023

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“…A hybrid deep learning approach for skin lesion segmentation was devised by [30], wherein the ResNet and AlexNet were combined together for enhancing the segmentation accuracy. Initially, the color constancy of the image was enhanced through the pre-processing stage using the gray world algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…The comparative Assessment of the proposed method with the conventional skin lesion segmentation approaches like ResNet+ UNet [32], ResNet + AlexNet [30], DS-TransUNet [29], and DE-ABC [33]. The assessment based on various evaluation measures is depicted in Fig.…”

Section: Comparative Assessmentmentioning

confidence: 99%

Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization

Ahmed¹,

Jalil²,

Usman³

2023

IJACSA

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Skin cancers have been on an upward trend, with melanoma being the most severe type. A growing body of investigation is employing digital camera images to computeraided examine suspected skin lesions for cancer. Due to the presence of distracting elements including lighting fluctuations and surface light reflections, interpretation of these images is typically difficult. Segmenting the area of the lesion from healthy skin is a crucial step in the diagnosis of cancer. Hence, in this research an optimized deep learning approach is introduced for the skin lesion segmentation. For this, the EfficientNet is integrated with the UNet for enhancing the segmentation accuracy. Also, the Improved Tuna Swarm Optimization (ITSO) is utilized for adjusting the modifiable parameters of the U-EfficientNet to minimize the information loss during the learning phase. The proposed ITSU-EfficientNet is assessed based on various evaluation measures like Accuracy, Mean Square Error (MSE), Precision, Recall, IoU, and Dice Coefficient and acquired the values are 0.94, 0.06, 0.94, 0.94, 0.92 and 0.94 respectively.

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“…The recognition and localization of skin cancer are required to compute the image feature for detecting cancer [12]. In which the CNN shows remarkable performance for processing and analyzing medical images [13]. This technique enhances the accessibility in underserved areas which ensures timely assessment and consultation [14].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network with Coordinate Attention Module for the Detection of Skin Cancer

2024

IJIES

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Skin cancer is one of the most common types of cancer globally, by increasing occurrence rates each year. It is a predominant kind of cancer which rises from the uncontrolled growth of abnormal skin cells due to genetic mutations including various factors such as UV radiation, genetics and other factors. The death rate is decreased when skin cancer is detected at early stages. Therefore, this paper proposed a Convolutional Neural Network (CNN) with Coordinate Attention Module (CAM) for early detection of skin cancer. The data augmentation is utilized in this experiment for data pre-processing and fed into the Gray Level Co-occurrence Matrix (GLCM) based feature extraction technique. The Harris Hawk Optimization (HHO) is utilized for selecting features that have faster convergence and strong capability in local optima. The selected features are given as input to CNN with the CAM approach. This model is estimated on ISIC-2019 and ISIC-2020 datasets and attains better results using accuracy, precision, recall, specificity, and F1-score. The obtained result shows that the proposed model achieves better accuracy of 98.77% on the ISIC-2019 dataset and 99.51% on the ISIC-2020 dataset which ensures accurate detection compared to other existing methods like Inception-ResNet and Residual Deep Convolutional Neural Network (RDCNN).

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An automatic skin lesion segmentation system with hybrid FCN-ResAlexNet

Cited by 12 publications

References 57 publications

Fuzzy Logic with Deep Learning for Detection of Skin Cancer

Fuzzy Logic with Deep Learning for Detection of Skin Cancer

Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization

Convolutional Neural Network with Coordinate Attention Module for the Detection of Skin Cancer

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