Skin Cancer Diagnosis Using CNN Features with Genetic Algorithm and Particle Swarm Optimization Methods

Başaran, Erdal; Çelik, Yüksel

doi:10.21203/rs.3.rs-1330798/v1

Cited by 3 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study [22], they came up with a way to spot early skin cancer. If the disease isn't treated early, it could spread to the area below the skin, making it hard to treat.…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

See 1 more Smart Citation

خوارزميات ذکاء السرب لتحسين في تشخيص الأمراض: مسح

علي,

امام,

عزام

2023

النشرة المعلوماتية في الحاسبات والمعلومات

View full text Add to dashboard Cite

For quick diagnosis, treatment, and healing, many diseases need to be caught early. Delays in diagnosis can lead to other risks. Recently, researchers have been using artificial intelligence to find many diseases quickly and accurately. In particular, they have been using machine learning, CNN, and optimisation algorithms to pick the right features for a simple training model for the classification stage. As most data sets have noisy and repetitive features in all application areas, this slows down the performance of the classifier and may even make the classification less accurate because the search space is so big. This also affects the runtime of the classification. This review gives a full look at Particle Swarm Optimisation (PSO), Artificial Bee Colony (ABC), and Grey Wolf Optimisation (GWO). It will also talk about how these methods can be used to diagnose diseases like skin cancer, adrenal gland tumours, diabetes, coronary heart disease, and others. Also, the different procedures that researchers have taken to improve the accuracy and speed of diagnosis, the changes they have made to these algorithms, hybrids of these algorithms, and proposed future trends in every search The base of this study is to help new researchers get an overview of swarm intelligence algorithms and their role in diagnosing diseases and to brighten their horizons for the future directions in this field.

show abstract

“…In this study [22], they came up with a way to spot early skin cancer. If the disease isn't treated early, it could spread to the area below the skin, making it hard to treat.…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…This model achieved an accuracy of 89.17%. In future work, they suggest the use of other CNN technologies and different feature selection algorithms [22].…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

خوارزميات ذکاء السرب لتحسين في تشخيص الأمراض: مسح

علي,

امام,

عزام

2023

النشرة المعلوماتية في الحاسبات والمعلومات

View full text Add to dashboard Cite

show abstract

Enhanced Classification of Skin Lesions Using Fine-Tuned MobileNet and DenseNet121 Models with Ensemble Learning

Sancar

2024

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

View full text Add to dashboard Cite

This study presents a deep learning approach for early detection of melanoma, one of the most dangerous skin cancers. In this article, all pre-trained models of the Keras library are trained with the ISIC skin cancer dataset available on Kaggle and the accuracy of each model is analyzed in detail. With the results obtained from the trained models, the models were fine-tuned to further optimize the performance of each model. After re-evaluation with fine-tuning, the accuracy rates were compared: DenseNet121 and MobileNet were found to be the two best models with high accuracy among the fine-tuned models. As such, these two models were combined in an ensemble approach to achieve a better overall accuracy. The skin cancer detection rate obtained with this ensemble approach is 93.03%. Therefore, the deep learning-based ensemble method appears to be a reliable and powerful technique that can be used to diagnose serious diseases such as skin cancer. This model can be used to provide a powerful support system with great potential to assist dermatologists in the early detection phase by easing workload and improving patient outcomes.

show abstract

Skin Cancer Prediction using Enhanced Genetic Algorithm with Extreme Learning Machine

Ramya¹,

Sathiyabhama²

2023

JTCSST

View full text Add to dashboard Cite

In the current scenario, the death rate due to the cause of skin cancer is increasing enormously. Diagnosis and prediction of Skin Cancer (SC) have become vital at an earlier stage. The main objective of this research is ensemble machine learning with enhanced genetic algorithm technique to achieve higher accuracy in the prediction of skin cancer at an earlier stage compared to other existing techniques. Although many machine learning and deep learning approaches implemented in detecting skin cancer at an earlier stage still there are few limitations. To overcome these problems in our proposed work, the CNN model, ResNet-16 usually produces successful results in extracting the features automatically and classifying the images very accurately. Therefore, the ResNet model used in our work obtains the deep features with the help of a fully connected layer. Later the feature selection is performed with the help of an Enhanced Genetic Algorithm (EGA) that produces optimized solutions by implementing operations like mutations, crossover, and ensemble with Extreme Learning Machine (EGA-ELM) to classify the images as either melanoma or non-melanoma. The proposed model certainly achieved higher accuracy and effective performance. Finally, the obtained results are to be compared with other popular classifying algorithms like Support Vector Machine (SVM) and various other models.

show abstract

Skin Cancer Diagnosis Using CNN Features with Genetic Algorithm and Particle Swarm Optimization Methods

Cited by 3 publications

References 23 publications

خوارزميات ذکاء السرب لتحسين في تشخيص الأمراض: مسح

خوارزميات ذکاء السرب لتحسين في تشخيص الأمراض: مسح

Enhanced Classification of Skin Lesions Using Fine-Tuned MobileNet and DenseNet121 Models with Ensemble Learning

Skin Cancer Prediction using Enhanced Genetic Algorithm with Extreme Learning Machine

Contact Info

Product

Resources

About