Binary Classification of Melanoma Skin Cancer using SVM and CNN

Tanna, Riya; Sharma, Toshita

doi:10.1109/aimv53313.2021.9670894

Cited by 6 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the model is improved by a higher AUC. It utilizes positive and negative numbers, demonstrating its capacity for classification [27]. The corresponding coordinates for the threshold's highest value are (0, 0), and for the threshold's smallest value are (1, 1) [25].…”

Section: Recall= (6)mentioning

confidence: 99%

Post Pandemic Tourism: Sentiment Analysis using Support Vector Machine Based on TikTok Data

Sabri,

Subki,

Bahrin

et al. 2024

IJACSA

View full text Add to dashboard Cite

The tourism industry is one of the hard hit businesses during the Covid-19 pandemic and has been struggling for backup ever since. However, nowadays the industry has started to bloom again with the lifting of all of the restrictions of Covid-19. This research aims to analyze the sentiments of the tourists using the Support Vector Machine (SVM) algorithm to know their views on the tourist spots after the pandemic. The scope of the research covers the state of Terengganu which is popularly known for its islands and unique culture on the east coast of Malaysia. TikTok data has been used as the source of data as social media currently has become one of the top mediums for reviewing, selling and promoting products and services. The objective of the research is to explore the SVM algorithm in the sentiment classification of tourist spots in Terengganu. This research is expected to help the Tourism Terengganu to improve their tourist spots and their services. The phases of the research include collecting data from TikTok, data pre-processing, data labelling, feature extraction, model creation using SVM, graphical user interface development and performance evaluation. The evaluation results showed that the performance of the SVM classifier model was good and reliable, with 90.68% accuracy. The future work would be collecting more data from TikTok regularly to further improve the accuracy of the algorithm.

show abstract

Section: Recall= (6)mentioning

confidence: 99%

Post Pandemic Tourism: Sentiment Analysis using Support Vector Machine Based on TikTok Data

Sabri,

Subki,

Bahrin

et al. 2024

IJACSA

View full text Add to dashboard Cite

show abstract

“…They employed HAM10000 dataset in their research. Two methods for the diagnosis of skin cancers were proposed by (Tanna & Sharma 2021), specifically using imaging data from melanoma malignant cells. In the first method, threelayer convolutional neural networks were used, while in the second, a basic Support Vector Machine (SVM) model with the default Radial Basis Function kernel was employed.…”

Section: Literature Reviewmentioning

confidence: 99%

Skin Cancer Detection: Leveraging Convolutional Neural Networks for Binary Classification

Swaroop,

Charan Kumari,

Srinivas

2024

J. Tr., Chal. Art. Intell.

View full text Add to dashboard Cite

Globally Skin cancer accounts for millions of new cases annually posing a significant threat to public health, emphasizing the importance of timely detection to enhance patient outcomes and decrease mortality rates. In this research article an AI convolutional neural network (CNN) model for the detection and classification of skin lesions into benign and malignant type is presented. A comprehensive dataset comprising 33,126 dermoscopic images from the ISIC 2020 challenge, a tailored CNN architecture was developed and validated for accurate lesion classification. The good performance of the developed CNN model on various benchmark metrics highlight its capacity to precisely distinguish between benign and malignant skin lesions.The outcomes of this investigation indicate that the developed CNN model has the potential to significantly enhance diagnostic protocols by minimizing manual errors and streamlining patient care pathways through automation. Through the amalgamation of sophisticated machine learning methodologies with clinical protocols, the model not only supports immediate clinical implementations but also lays the groundwork for future advancements by incorporating larger datasets and real-world clinical information. This study provides a scalable CNN model for building an automated tool that can integrate into healthcare systems to facilitate early detection and treatment of skin cancer,resulting in cost reductions and improved health outcomes. This endeavor makes a remarkable impact in the realm of dermatology by emphasizing the utilization of artificial intelligence to transform the diagnosis of skin cancer and enhance patient care.

show abstract

“…We can observe from the review that many of the authors proposed CNN's for detecting the Melanoma skin cancer. Tanna and Sharma [4] proposed a 3 layer CNN, which was able to achieve the accuracy of 84.39%. Abdullah et al, [5] proposed a new algorithm based on CNN, which used the straight active-contour and morphological processes to divide the cutaneous lesion into cutaneous images and was tested in the Al-Kindi Hospital and Baghdad Medical City's real database.…”

Section: Traditional Cnnmentioning

confidence: 99%

Convolution Neural Network Models to Detect Melanoma: A Review

Naveen¹,

Kumar²,

Haw³

et al. 2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

View full text Add to dashboard Cite

Skin cancer is one of the most serious health issues that humans face. Dermatologists face difficulty in making a skin cancer diagnosis because many skin cancer pigments seem alike. Early detection of skin cancers like Melanoma means a better chance of survival for the patient otherwise it can be life-threatening. For computer vision problems like image classification, deep learning has proven to be the state-of-the-art. There has been a great deal of research into the use of deep learning to automate skin cancer screening. The objective of this paper is to review the state-of-the-art CNN techniques used for Melanoma detection. This paper presents an overview of CNN, followed by analysing the existing work carried out in the area of Melanoma skin cancer detection using Convolution Neural Network (CNN).

show abstract

Binary Classification of Melanoma Skin Cancer using SVM and CNN

Cited by 6 publications

References 9 publications

Post Pandemic Tourism: Sentiment Analysis using Support Vector Machine Based on TikTok Data

Post Pandemic Tourism: Sentiment Analysis using Support Vector Machine Based on TikTok Data

Skin Cancer Detection: Leveraging Convolutional Neural Networks for Binary Classification

Convolution Neural Network Models to Detect Melanoma: A Review

Contact Info

Product

Resources

About