WITHDRAWN: Systematic investigation on Multi-Class skin cancer categorization using machine learning approach

Kumar, Sunil; Ismail, B. Mohammed

doi:10.1016/j.matpr.2020.11.484

Cited by 11 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dermatologists suggest one among three treatments depending on the lesion type: surgery, home care, or drugs. A specific sort of skin lesion can be dangerous to the victims as they denote malevolence and need surgery [3]. One of the risky kinds of skin cancer (SC) is Melanoma; as soon as it has spread, it is lethal; but it was curable in its initial stage.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

Srilakshmi¹,

Lydia²,

Ramakrishnaiah

2023

Preprint

View full text Add to dashboard Cite

Skin cancer (SC) is the most prevalent cancer globally. Clinical assessment of skin lesions is needed to assess the disease characteristics; but, it is varied in interpretation and limited by longer timelines. Dermoscopy refers to a non-invasive imaging method that permits dermatologists to examine skin lesions with improved visualization of surface and subsurface structures. Machine-learning (ML) and deep learning (DL) methods were established to support dermatologists and overcome the problem as accurate and earlier diagnoses of SC is critical to increasing survival rate of the patient. In recent years, SC classification on dermoscopy images using DL has received great deal of interest. DL algorithms, especially convolutional neural networks (CNNs), have proved promising outcomes in precisely classifying skin lesions and differentiating between benign and malignant cases. The study presents a new Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification (DBOA-MMDLSCC) technique on dermoscopic images. The presented DBOA-MMDLSCC technique exploits the ensemble learning mechanism by the use of three DL models with metaheuristic based hyperparameter tuning. In the presented DBOA-MMDLSCC technique, U-Net++ model is utilized for skin lesion segmentation. For feature extraction, multi-modal DL model comprising three DL models namely Xception, Residual Network (ResNet), and SqueezeNet. Moreover, the hyperparameter tuning of the DL approaches takes place using the DBOA. Lastly, convolutional autoencoder (CAE) model is applied for detecting and classifying SC. A wide range of experiments were performed to exhibit the simulation results of the DBOA-MMDLSCC technique. The experimental values highlighted the improved performance of the DBOA-MMDLSCC technique in terms of different evaluation measures.

show abstract

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

Srilakshmi¹,

Lydia²,

Ramakrishnaiah

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A melanoma diagnosed at an early stage has a 90% chance of being treated, compared with 50% if the cancer is diagnosed at a late stage [10]. Increasingly high-resolution imaging technologies have been developed non-invasively, it is now easier to diagnose skin cancer or lesions in-situ [4].…”

Section: International Journal Of Research In Science and Technologymentioning

confidence: 99%

Deep Learning and MachineLearning to Diagnose Melanoma

Kaushik¹

2023

IJRST

View full text Add to dashboard Cite

The most dangerous disorders include melanoma. Yet, a precise diagnosis of skin cancer is difficult. Recent research has shown that a variety of activities can be performed better using deep learning and machine learning techniques. For skin conditions, these algorithms are highly useful. In this article, we examine various deep learning and machine learning techniques and how they could be applied to the detection of melanoma. This paper provides a number of publicly downloadable datasets, information on common melanoma, instructions for getting dermatology pictures, and more. Once machine learning and deep learning concepts have been introduced, our attention shifts to analysing common machine learning and deep learning architectures as well as popular frameworks for putting machine and deep learning algorithms into practice. Metrics for performance evaluation are then offered. In this section, we will cover the research on machine learning and deep learning and how they can be applied to the detection of melanoma skin illnesses. We also go over potential research avenues and the difficulties in the field. The main objective of this work is to discuss modern machine learning and deep learning techniques for melanoma diagnosis.

show abstract

“…This work provides an idea to process the classification, detection, and segmentation of skin cancer and the skin cancer affected area utilizing a hybrid image processing approach. The k-means algorithm, watershed method, and the difference in strength methods [28][29]. Initially, the image has been segmented into the different regions by using the k-means clustering approach.…”

Section: Data Mining Techniquesmentioning

confidence: 99%

A Systematic Study And Approach On Detection Of Classification Of Skin Cancer Using Back Propagated Artificial Neural Networks

Nyemeesha¹

2021

TURCOMAT

View full text Add to dashboard Cite

Skin cancer is the leading type of cancer that causes millions of deaths of human beings. Early identification and appropriate medications for new harmful skin malignancy cases are fundamental to guarantee a low death rate as the survival rate. Most of the related works are focusing on machine learning-based algorithms, but they provide the maximum accuracy and specificity. In the preprocessing stage, sharpening filter and smoothening filters are used to remove the noise along with enhancement operations. Then segmentation is used for efficient detection of the region of skin cancer. Finally, to achieve the maximum accuracy for classification back-propagated based artificial neural network (BP-ANN) developed for the classification of skin cancer with the spatially gray level dependency matrix (SGLD) features. The suggested research work can be effectively used for the classification of Benign and Melanoma skin cancers.

show abstract

WITHDRAWN: Systematic investigation on Multi-Class skin cancer categorization using machine learning approach

Cited by 11 publications

References 31 publications

WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

WITHDRAWN: Dung Beetle Optimization Algorithm with Multi-modal Deep Learning based Skin Cancer Classification on Dermoscopic Images

Deep Learning and MachineLearning to Diagnose Melanoma

A Systematic Study And Approach On Detection Of Classification Of Skin Cancer Using Back Propagated Artificial Neural Networks

Contact Info

Product

Resources

About