ArticleDiagnostics (Basel, Switzerland)2023
Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- HRKHu-Loss: harmonic regularization Kullback Huber loss-based lung cancer detection and severity level classification using CT image.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Programmed death 1 antibody drug conjugates (PD 1 ADCs): Innovations in overcoming resistance and enhancing immune mediated antitumor activity.Translational oncology · 2026Review
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- Interpretable hybrid ensemble with attention-based fusion and EAOO-GA optimization for lung cancer detection.Scientific reports · 2026Article
- Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer usingClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Efficacy of pulmonary rehabilitation combined with minimal-dose adjunctive TCM (Shen Yi Capsules and Hua Chan Su) in improving postoperative outcomes for non-small cell lung cancer: a randomized controlled trial.Frontiers in medicine · 2026Article
- An Adaptive Hybrid Metaheuristic Algorithm for Lung Cancer in Pathological Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning for Tumor Segmentation and Multiclass Classification in Breast Ultrasound Images Using Pretrained Models.Sensors (Basel, Switzerland) · 2025Article
- Multi - scale context U-net for breast cancer postoperative radiotherapy in patients with brachial plexus.BMC medical imaging · 2025Article
- Automated segmentation of canine pulmonary masses in CT imaging using AI.The veterinary quarterly · 2025Article
- Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.BMC medical informatics and decision making · 2025Article
- Automated classification and explainable AI analysis of lung cancer stages using EfficientNet and gradient-weighted class activation mapping.Frontiers in medicine · 2025Article
- Optimized deep learning approach for lung cancer detection using flying fox optimization and bidirectional generative adversarial networks.PeerJ. Computer science · 2025Article
- The development of an attention mechanism enhanced deep learning model and its application for body composition assessment with L3 CT images.Scientific reports · 2024Article
- Applications of Deep Learning Techniques in Healthcare Systems: A Review.Journal of clinical practice and research · 2024Review
- Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning.Scientific reports · 2024Article
- Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam.BMC medical imaging · 2024Article
- Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.Journal of imaging · 2024Article
- An Advanced Lung Carcinoma Prediction and Risk Screening Model Using Transfer Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Exploring Fracture Patterns: Assessing Representation Methods for Bone Fracture Simulation.Journal of personalized medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Lung cancer presents one of the leading causes of mortalities for people around the world. Lung image analysis and segmentation are one of the primary steps used for early diagnosis of cancer. Handcrafted medical imaging segmentation presents a very time-consuming task for radiation oncologists. To address this problem, we propose in this work to develop a full and entire system used for early diagnosis of lung cancer in CT scan imaging. The proposed lung cancer diagnosis system is composed of two main parts: the first part is used for segmentation developed on top of the UNETR network, and the second part is a classification part used to classify the output segmentation part, either benign or malignant, developed on top of the self-supervised network. The proposed system presents a powerful tool for early diagnosing and combatting lung cancer using 3D-input CT scan data. Extensive experiments have been performed to contribute to better segmentation and classification results. Training and testing experiments have been performed using the Decathlon dataset. Experimental results have been conducted to new state-of-the-art performances: segmentation accuracy of 97.83%, and 98.77% as classification accuracy. The proposed system presents a new powerful tool to use for early diagnosing and combatting lung cancer using 3D-input CT scan data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.