Advancing Skin Cancer Prediction Using Ensemble Models

Computers, Journal Year: 2024, Volume and Issue: 13(7), P. 157 - 157

Published: June 21, 2024

There are many different kinds of skin cancer, and an early precise diagnosis is crucial because cancer both frequent deadly. The key to effective treatment accurately classifying the various cancers, which have unique traits. Dermoscopy other advanced imaging techniques enhanced detection by providing detailed images lesions. However, interpreting these distinguish between benign malignant tumors remains a difficult task. Improved predictive modeling necessary due occurrence erroneous inconsistent outcomes in present diagnostic processes. Machine learning (ML) models become essential field dermatology for automated identification categorization lesions using image data. aim this work develop improved predictions ensemble models, combine numerous machine approaches maximize their combined strengths reduce individual shortcomings. This paper proposes fresh special approach model optimization classification: Max Voting method. We trained assessed five ISIC 2018 HAM10000 datasets: AdaBoost, CatBoost, Random Forest, Gradient Boosting, Extra Trees. Their enhance overall performance with Moreover, were fed feature vectors that optimally generated from data genetic algorithm (GA). show that, accuracy 95.80%, significantly improves when compared individually. Obtaining best results F1-measure, recall, precision, method turned out be most dependable robust. novel aspect more robustly reliably classified technique. Several pre-trained models’ benefits approach.

Language: Английский

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Alzheimer’s disease unveiled: Cutting-edge multi-modal neuroimaging and computational methods for enhanced diagnosis

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 97, P. 106721 - 106721

Published: Aug. 8, 2024

Language: Английский

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OCTNet: A Modified Multi-Scale Attention Feature Fusion Network with InceptionV3 for Retinal OCT Image Classification

Mathematics, Journal Year: 2024, Volume and Issue: 12(19), P. 3003 - 3003

Published: Sept. 26, 2024

Classification and identification of eye diseases using Optical Coherence Tomography (OCT) has been a challenging task trending research area in recent years. Accurate classification detection different are crucial for effective care management improving vision outcomes. Current methods fall into two main categories: traditional deep learning-based approaches. Traditional approaches rely on machine learning feature extraction, while utilize data-driven model training. In years, Deep Learning (DL) Machine (ML) algorithms have become essential tools, particularly medical image classification, widely used to classify identify various diseases. However, due the high spatial similarities OCT images, accurate remains task. this paper, we introduce novel called “OCTNet” that integrates combining InceptionV3 with modified multi-scale attention-based attention block enhance performance. OCTNet employs an backbone fusion dual modules construct proposed architecture. The generates rich features from capturing both local global aspects, which then enhanced by utilizing block, resulting significantly improved map. To evaluate model’s performance, utilized state-of-the-art (SOTA) datasets include images normal cases, Choroidal Neovascularization (CNV), Drusen, Diabetic Macular Edema (DME). Through experimentation simulation, improves accuracy 1.3%, yielding higher than other SOTA models. We also performed ablation study demonstrate effectiveness method. achieved overall average 99.50% 99.65% datasets.

Language: Английский

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Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural network

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 12, 2025

Abstract The cross-border intelligent marketing algorithm based on traditional linear models is relatively single in information feature extraction, making it difficult to effectively handle complex scenarios containing a large amount of implicit users and markets, resulting poor personalized effectiveness. To address this issue, article proposes model that integrates rating user labels using multi-layer perceptron grey wolf optimization convolutional neural network (MLP-GWO-CNN). This extracts high-order through nonlinear methods can sparse data. Firstly, dual path deep structure was designed, which one modeled (MLP) extract interest features historical interaction ratings; Another utilizes Convolutional Neural Networks (CNN) semantic from label construct item representations. In response the sensitivity MLP initial values its tendency fall into local optima, paper uses GWO optimize MLP. Next, latent vectors generated by CNN are fused output layer generate final predictive strategy last. Experiments were conducted real e-commerce dataset, results showed compared with recommendation algorithms, MLP-GWO-CNN proposed performs better utilizing tag information, improving accuracy personalization recommendations. over 89%, recall rate 90%.

Language: Английский

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Differential evolution-driven optimized ensemble network for brain tumor detection

International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Language: Английский

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