Detection of stages of melanoma using deep learning

Kumar, N. M. Saravana; Hariprasath, K.; Tamilselvi, S.; Kavinya, A.; Kaviyavarshini, N.

doi:10.1007/s11042-021-10572-1

Cited by 17 publications

(7 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their 2021 study, Kumar et al [57] employed DL methods to determine the incidence of skin cancer. Ali et al [58] developed a deep CNN and denoised the HAM10000 dataset to eliminate unwanted features, such as air bubbles and artifacts, with the intention of diagnosing skin cancer.…”

Section: Deep Learning Featuresmentioning

confidence: 99%

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

Naeem,

Anees,

Khalil

et al. 2024

Mathematics

View full text Add to dashboard Cite

The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection.

show abstract

Section: Deep Learning Featuresmentioning

confidence: 99%

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

Naeem,

Anees,

Khalil

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…In 2018, approximately 300,000 new cases of melanoma were detected globally, making it the most prevalent cancer among both men and women [53]. Over one million new cases of Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) were diagnosed in the same year, ranking them as the second and third most common forms of skin cancer after melanoma, respectively.…”

Section: Prevalence Of Skin Lesion Disordersmentioning

confidence: 99%

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

Mukhlif,

Ramaha,

Hameed

et al. 2024

Mathematics

View full text Add to dashboard Cite

The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients.

show abstract

“…Its relation to the depth of the lesion is yet to be studied. The study by [14] used the sum rule fusion method and ANN to confirm whether the melanoma stage is critical. However, this method does not clarify the distinction between each stage.…”

Section: Models and Xai For Melanoma Detectionmentioning

confidence: 99%

The Depth Estimation and Visualization of Dermatological Lesions: A Novel Methodology (Preprint)

Parekh,

Oyeleke

2024

Preprint

View full text Add to dashboard Cite

BACKGROUND Thus far, considerable research has been focused on classifying a lesion as benign or malignant. OBJECTIVE To propose a novel methodology for the depth estimation and visualization of skin lesions. METHODS We start by doing the same using a CNN, followed by using Explainable AI (XAI) to localize the image features responsible for the CNN output. We apply computer graphics for depth estimation and developing the 3D structure of the lesion. Our novel method, called the red spot analysis, measures the degree of infection based on which a conical hologram is constructed. Physicians can study this hologram via Mixed Reality headsets. RESULTS The neural model achieves an accuracy of 85.61%. We successfully obtained 3D representations of lesion depth using the method stated above. CONCLUSIONS When we map the CNN outputs (benign or malignant) to the corresponding hologram, we observe that a malignant lesion has a higher concentration of red spots (infection) in the upper and deeper portions of the skin. CLINICALTRIAL We do not perform RCT for this study.

show abstract

Detection of stages of melanoma using deep learning

Cited by 17 publications

References 22 publications

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

Ant Colony and Whale Optimization Algorithms Aided by Neural Networks for Optimum Skin Lesion Diagnosis: A Thorough Review

The Depth Estimation and Visualization of Dermatological Lesions: A Novel Methodology (Preprint)

Contact Info

Product

Resources

About