Breast cancer classification based on hybrid CNN with LSTM model

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 5, 2025

Breast cancer (BC) is a global problem, largely due to shortage of knowledge and early detection. The speed-up process detection classification crucial for effective treatment. Medical image analysis methods computer-aided diagnosis can enhance this process, providing training assistance less experienced clinicians. Deep Learning (DL) models play great role in accurately detecting classifying the huge dataset, especially when dealing with large medical images. This paper presents novel hybrid model DL combined Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) binary breast on two datasets available at Kaggle repository. CNNs extract mammographic features, including spatial hierarchies malignancy patterns, whereas LSTM networks characterize sequential dependencies temporal interactions. Our method combines these structures improve accuracy resilience. We compared proposed other models, such as CNN, LSTM, Gated Recurrent Units (GRUs), VGG-16, RESNET-50. CNN-LSTM achieved superior performance accuracies 99.17% 99.90% respective datasets. uses prediction evaluation metrics accuracy, sensitivity, specificity, F-score, AUC curve. results showed that our classifiers others second dataset.

Язык: Английский

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A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics

Journal of Cancer Research and Clinical Oncology, Год журнала: 2023, Номер 149(15), С. 14365 - 14408

Опубликована: Авг. 4, 2023

Язык: Английский

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DBL-Net: A dual-branch learning network with information from spatial and frequency domains for tumor segmentation and classification in breast ultrasound image

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 93, С. 106221 - 106221

Опубликована: Март 18, 2024

Язык: Английский

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Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis

Diagnostics, Год журнала: 2024, Номер 14(12), С. 1265 - 1265

Опубликована: Июнь 15, 2024

Rapid advancements in artificial intelligence (AI) and machine learning (ML) are currently transforming the field of diagnostics, enabling unprecedented accuracy efficiency disease detection, classification, treatment planning. This Special Issue, entitled “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis”, presents a curated collection cutting-edge research that explores integration AI ML technologies into various diagnostic modalities. The contributions presented here highlight innovative algorithms, models, applications pave way improved capabilities across range medical fields, including radiology, pathology, genomics, personalized medicine. By showcasing both theoretical practical implementations, this Issue aims to provide comprehensive overview current trends future directions AI-driven fostering further collaboration dynamic impactful area healthcare. We have published total 12 articles all collected between March 2023 December 2023, comprising 1 Editorial cover letter, 9 regular articles, review article, article categorized as “other”.

Язык: Английский

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XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Ноя. 19, 2024

Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, women's health. The present study outlines a novel approach for analyzing data by using CatBoost classification model with multi-layer perceptron neural network (CatBoost+MLP). Explainable artificial intelligence techniques are used to cohere proposed MLP model. aims enhance interpretability predictions leveraging benefits technique feature identification and also contributing towards decision CatBoost+MLP has been evaluated Shapley additive explanations values analyze significance decision-making. Initially, engineering done analysis variance identify significant features. alone being analyzed divergent performance metrics, results obtained compared contemporary techniques.

Язык: Английский

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Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

Biomedical Physics & Engineering Express, Год журнала: 2024, Номер 10(3), С. 035029 - 035029

Опубликована: Апрель 10, 2024

Abstract A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly women, the incidence breast cancer is rising daily because ignorance and delayed diagnosis. Only by correctly identifying diagnosing its very early stages development can be effectively treated. The classification accelerated automated aid computer-aided diagnosis medical image analysis techniques. This research provides use transfer learning from a Residual Network 18 (ResNet18) 34 (ResNet34) architectures to detect cancer. study examined how identified mammography pictures using ResNet18 ResNet34, developed demo app for radiologists trained models best validation accuracy. 1, 200 datasets x-ray images National Radiological Society’s (NRS) archives were employed study. dataset was categorised as implant negative, positive, negative positive order increase consistency produce better features. For multi-class images, gave an average accuracy binary benign or malignant cases 86.7% ResNet34 92% ResNet18. prototype web application showcasing performance has been created. acquired results show improve detection, providing invaluable assistance professionals, African scenario.

Язык: Английский

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Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 11, 2025

Язык: Английский

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LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection

Bioengineering, Год журнала: 2025, Номер 12(1), С. 73 - 73

Опубликована: Янв. 15, 2025

Breast cancer ranks as the second most prevalent globally and is frequently diagnosed among women; therefore, early, automated, precise detection essential. Most AI-based techniques for breast are complex have high computational costs. Hence, to overcome this challenge, we presented innovative LightweightUNet hybrid deep learning (DL) classifier accurate classification of cancer. The proposed model boasts a low cost due its smaller number layers in architecture, adaptive nature stems from use depth-wise separable convolution. We employed multimodal approach validate model’s performance, using 13,000 images two distinct modalities: mammogram imaging (MGI) ultrasound (USI). collected datasets seven different sources, including benchmark DDSM, MIAS, INbreast, BrEaST, BUSI, Thammasat, HMSS. Since various resized them uniform size 256 × pixels normalized Box-Cox transformation technique. USI dataset smaller, applied StyleGAN3 generate 10,000 synthetic images. In work, performed separate experiments: first on real without augmentation + GAN-augmented our method. During experiments, used 5-fold cross-validation method, obtained good results (87.16% precision, 86.87% recall, 86.84% F1-score, accuracy) adding any extra data. Similarly, experiment provides better performance (96.36% 96.35% accuracy). This approach, which utilizes LightweightUNet, enhances by 9.20% 9.48% 9.51% increase accuracy combined dataset. works very well thanks creative network design, fake data, training These show that has lot potential clinical settings.

Язык: Английский

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Ensemble Machine Learning Algorithms for Precision Breast Cancer Diagnosis: A Multi-criteria Evaluation Approach

SN Computer Science, Год журнала: 2025, Номер 6(2)

Опубликована: Фев. 11, 2025

Язык: Английский

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Forecasting Breast Cancer with Integrated Pre-trained CNN and Machine Learning Framework from CT Images

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 455 - 466

Опубликована: Янв. 1, 2025

Язык: Английский

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