Recent innovations in machine learning for skin cancer lesion analysis and classification: A comprehensive analysis of computer‐aided diagnosis

Precision Medical Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Abstract The global primary health concern of skin cancer emphasizes the need for quick and accurate diagnosis to improve patient outcomes. Although, it might be challenging evaluate possible risk a spot merely by looking at feeling it. This review article offers thorough overview current breakthroughs in machine learning (ML) computer‐aided diagnostics (CAD) aim analysis classification lesions over past 6 years. paper carefully reviews whole diagnostic process: data preparation, lesion segmentation, feature extraction, selection, final classification. Analyzed are many publicly accessible datasets creative ideas including deep (DL) ML integrated with computer vision, together their impact on increasing accuracy. Given variety complexity lesions, even enormous progress, there still major obstacles. rigorously assesses methods, notes areas great challenge, provides recommendations direct next research targeted improving early detection strategies CAD systems.

Language: Английский

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Fusion of Deep and Time–Frequency Local Features for Melanoma Skin Cancer Detection

Applied Computational Intelligence and Soft Computing, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Skin cancer spreads quickly as the skin is most vulnerable organ, and melanoma (MEL) a fatal type of cancer. Detecting MEL in early stage can hugely increase chance cure. There are several methods based on machine learning to detect from dermoscopic images. However, increasing accuracy detection still challenging. This paper presents new method for that considers combination deep handcrafted time–frequency local features. After short preprocessing, convolutional neural networks (CNNs) extract To this end, feature maps at output flatten layer considered The scale‐invariant transform (SIFT) descriptors features computed four subbands one‐level two‐dimensional discrete wavelet (2D DWT). fusion mentioned features, semisupervised discriminant analysis (SDA) reduces highly correlated redundant Bayesian optimizer finds optimum parameters SDA Gaussian kernel support vector (SVM) classifier maximize classification accuracy. HAM10000 dataset with data augmentation assess performance proposed method. Simulation results show reaches sensitivity 94.19% 96.22%, respectively. challenging parts extraction tuning Gaussian‐SVM.

Language: Английский

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Systematic Review of Deep Learning Techniques in Skin Cancer Detection

BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(4), P. 2251 - 2270

Published: Nov. 14, 2024

Skin cancer is a serious health condition, as it can locally evolve into disfiguring states or metastasize to different tissues. Early detection of this disease critical because increases the effectiveness treatment, which contributes improved patient prognosis and reduced healthcare costs. Visual assessment histopathological examination are gold standards for diagnosing these types lesions. Nevertheless, processes strongly dependent on dermatologists’ experience, with excision advised only when suspected by physician. Multiple approaches have surfed over last few years, particularly those based deep learning (DL) strategies, goal assisting medical professionals in diagnosis process ultimately diminishing diagnostic uncertainty. This systematic review focused analysis relevant studies DL applications skin diagnosis. The qualitative included 164 records topic. AlexNet, ResNet-50, VGG-16, GoogLeNet architectures considered top choices obtaining best classification results, multiclassification current trend. Public databases key elements area should be maintained facilitate scientific research.

Language: Английский

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Enhancing Food Crop Classification in Agriculture through Dipper Throat Optimization and Deep Learning with Remote Sensing

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2024, Volume and Issue: 9, P. 100732 - 100732

Published: Aug. 15, 2024

Language: Английский

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Human Age Recognition Method Based on Facial Images Using an Ensemble of Neural Network Classifiers

Lecture notes on data engineering and communications technologies, Journal Year: 2024, Volume and Issue: unknown, P. 143 - 155

Published: Jan. 1, 2024

Language: Английский

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Anomaly Detection in Embryo Development and Morphology Using Medical Computer Vision-Aided Swin Transformer with Boosted Dipper-Throated Optimization Algorithm

Bioengineering, Journal Year: 2024, Volume and Issue: 11(10), P. 1044 - 1044

Published: Oct. 18, 2024

Infertility affects a significant number of humans. A supported reproduction technology was verified to ease infertility problems. In vitro fertilization (IVF) is one the best choices, and its success relies on preference for higher-quality embryo transmission. These have been normally completed physically by testing embryos in microscope. The traditional morphological calculation shows predictable disadvantages, including effort- time-consuming expected risks bias related individual estimations specific embryologists. Different computer vision (CV) artificial intelligence (AI) techniques devices recently applied fertility hospitals improve efficacy. AI addresses imitation intellectual performance capability technologies simulate cognitive learning, thinking, problem-solving typically Deep learning (DL) machine (ML) are advanced algorithms various fields considered main future human assistant technology. This study presents an Embryo Development Morphology Using Computer Vision-Aided Swin Transformer with Boosted Dipper-Throated Optimization (EDMCV-STBDTO) technique. EDMCV-STBDTO technique aims accurately efficiently detect development, which critical improving treatments advancing developmental biology using medical CV techniques. Primarily, method performs image preprocessing bilateral filter (BF) model remove noise. Next, swin transformer implemented feature extraction employs variational autoencoder (VAE) classify development. Finally, hyperparameter selection VAE boosted dipper-throated optimization (BDTO) efficiency validated comprehensive studies benchmark dataset. experimental result that better than recent

Language: Английский

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Investigating the effects of destructive factors on pulse repetition interval modulation type recognition using deep convolutional neural networks based on transfer learning

IET Radar Sonar & Navigation, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 6, 2024

Abstract Automation and self‐sufficiency in the complex environment of modern electronic warfare (EW) are critical necessary issues intelligence support systems to detect real‐time accurate threat radars. The task these is search, discover, analyse, identify parameters radar signals. However, recognition pulse repetition interval (PRI) modulation challenging natural environments due destructive factors, including missing pulses (MP), spurious (SP), large outliers (LO) (caused by antenna scanning), which lead noisy sequences PRI variation patterns. current article examines effects factors on recognising signals using deep convolutional neural networks (DCNNs). uses simulations based actual generate data consider with different percentages. number images obtained applying sum for each range (with percentages) considered 30,000. It common six types modulation. Then, DCNN models, VGG16, ResNet50V2, InceptionV3, Xception, MobileNetV2, trained transfer learning method. simulation results show that accuracy training testing models decreases significantly increase percentage factors. Also, model type performance have been investigated, shown some more resistant destruction retain accuracy. Finally, this analysis shows improve network (DNN) techniques face changes caused it pay attention apply appropriate strategies.

Language: Английский

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Optimizing Accurate Food Crop Classification Using Enhanced Dipper Throat Optimization and Deep Learning Models with Remote Sensing Images

Published: Jan. 1, 2024

Language: Английский

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