Machine learning based skin lesion segmentation method with novel borders and hair removal techniques

Rehman, Mohibur; Ali, Mushtaq; Obayya, Marwa; Asghar, Junaid; Hussain, Lal; Nour, Mohamed K.; Negm, Noha; Hilal, Anwer Mustafa

doi:10.1371/journal.pone.0275781

Cited by 6 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For each pixel in the image, a local neighborhood or window around that pixel is defined. The size of this window can vary based on the application and the expected size of the objects to be segmented [33]. The pixel's mean (average) intensity value is calculated within each local window.…”

Section: Pre-processing: Adaptive Thresholding-based Roi Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks

Behara,

Bhero,

Agee

2024

IJMS

View full text Add to dashboard Cite

Skin cancer is a severe and potentially lethal disease, and early detection is critical for successful treatment. Traditional procedures for diagnosing skin cancer are expensive, time-intensive, and necessitate the expertise of a medical practitioner. In recent years, many researchers have developed artificial intelligence (AI) tools, including shallow and deep machine learning-based approaches, to diagnose skin cancer. However, AI-based skin cancer diagnosis faces challenges in complexity, low reproducibility, and explainability. To address these problems, we propose a novel Grid-Based Structural and Dimensional Explainable Deep Convolutional Neural Network for accurate and interpretable skin cancer classification. This model employs adaptive thresholding for extracting the region of interest (ROI), using its dynamic capabilities to enhance the accuracy of identifying cancerous regions. The VGG-16 architecture extracts the hierarchical characteristics of skin lesion images, leveraging its recognized capabilities for deep feature extraction. Our proposed model leverages a grid structure to capture spatial relationships within lesions, while the dimensional features extract relevant information from various image channels. An Adaptive Intelligent Coney Optimization (AICO) algorithm is employed for self-feature selected optimization and fine-tuning the hyperparameters, which dynamically adapts the model architecture to optimize feature extraction and classification. The model was trained and tested using the ISIC dataset of 10,015 dermascope images and the MNIST dataset of 2357 images of malignant and benign oncological diseases. The experimental results demonstrated that the model achieved accuracy and CSI values of 0.96 and 0.97 for TP 80 using the ISIC dataset, which is 17.70% and 16.49% more than lightweight CNN, 20.83% and 19.59% more than DenseNet, 18.75% and 17.53% more than CNN, 6.25% and 6.18% more than Efficient Net-B0, 5.21% and 5.15% over ECNN, 2.08% and 2.06% over COA-CAN, and 5.21% and 5.15% more than ARO-ECNN. Additionally, the AICO self-feature selected ECNN model exhibited minimal FPR and FNR of 0.03 and 0.02, respectively. The model attained a loss of 0.09 for ISIC and 0.18 for the MNIST dataset, indicating that the model proposed in this research outperforms existing techniques. The proposed model improves accuracy, interpretability, and robustness for skin cancer classification, ultimately aiding clinicians in early diagnosis and treatment.

show abstract

Section: Pre-processing: Adaptive Thresholding-based Roi Extractionmentioning

confidence: 99%

“…The Taylor series equation is written as enhanced TS, and the updated solution is calculated as: R t+1 = 0.5R t + 1.359 R t−1 − 1.359 R t−2 + 0.6795 R t−3 (33) Equate the Equations ( 32) and ( 33…”

Section: Stashing Phasementioning

confidence: 99%

Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks

Behara,

Bhero,

Agee

2024

IJMS

View full text Add to dashboard Cite

show abstract

“…Still, the image with low contrast and color variations affects the performance of the model. Skin lesion segmentation using the Grab cut approach was devised by [31], wherein the skin hair removal and corner borders removal were devised in the image pre-processing for enhancing the performance of the model. In the hair removal task, the detection of hair contour was devised and then the mask was generated for removing the hair from the input image.…”

Section: Related Workmentioning

confidence: 99%

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

Ahmed¹,

Jalil²,

Usman³

2023

IJACSA

View full text Add to dashboard Cite

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.

show abstract

A survey, review, and future trends of skin lesion segmentation and classification

Hasan

Ahamad

Yap

et al. 2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

Machine learning based skin lesion segmentation method with novel borders and hair removal techniques

Cited by 6 publications

References 28 publications

Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks

Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks

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

A survey, review, and future trends of skin lesion segmentation and classification

Contact Info

Product

Resources

About