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Deep Learning for Brain Tumor Segmentation and Classification: A Systematic Review of Methods and Trends 认领 引用 被引量：1
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作者 Ameer Hamza Robertas Damaševicius 《Computers, Materials & Continua》 SCIE EI 2026年第1期132-172,共41页
This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 20... This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers. 展开更多
关键词 Brain tumor segmentation brain tumor classification deep learning vision transformers hybrid models
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An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images 认领 引用 被引量：1
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作者 Kiran Jabeen Muhammad Attique Khan +4 位作者 Ameer Hamza Hussain Mobarak Albarakati Shrooq Alsenan Usman Tariq Isaac Ofori 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第3期842-857,共16页
Breast cancer is one of the major causes of deaths in women.However,the early diagnosis is important for screening and control the mortality rate.Thus for the diagnosis of breast cancer at the early stage,a computer-a... Breast cancer is one of the major causes of deaths in women.However,the early diagnosis is important for screening and control the mortality rate.Thus for the diagnosis of breast cancer at the early stage,a computer-aided diagnosis system is highly required.Ultrasound is an important examination technique for breast cancer diagnosis due to its low cost.Recently,many learning-based techniques have been introduced to classify breast cancer using breast ultrasound imaging dataset(BUSI)datasets;however,the manual handling is not an easy process and time consuming.The authors propose an EfficientNet-integrated ResNet deep network and XAI-based framework for accurately classifying breast cancer(malignant and benign).In the initial step,data augmentation is performed to increase the number of training samples.For this purpose,three-pixel flip mathematical equations are introduced:horizontal,vertical,and 90°.Later,two pretrained deep learning models were employed,skipped some layers,and fine-tuned.Both fine-tuned models are later trained using a deep transfer learning process and extracted features from the deeper layer.Explainable artificial intelligence-based analysed the performance of trained models.After that,a new feature selection technique is proposed based on the cuckoo search algorithm called cuckoo search controlled standard error mean.This technique selects the best features and fuses using a new parallel zeropadding maximum correlated coefficient features.In the end,the selection algorithm is applied again to the fused feature vector and classified using machine learning algorithms.The experimental process of the proposed framework is conducted on a publicly available BUSI and obtained 98.4%and 98%accuracy in two different experiments.Comparing the proposed framework is also conducted with recent techniques and shows improved accuracy.In addition,the proposed framework was executed less than the original deep learning models. 展开更多
关键词 augmentation breast cancer classification deep learning optimization ultrasound images
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Role of targeted neuromodulation in the treatment of congenital unilateral lower lip palsy:A clinical case report 认领 引用
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作者 Hafiz Saqib Sikandar Ali Hassan Ameer Hamza 《Chinese Journal of Plastic and Reconstructive Surgery》 2024年第3期139-141,共3页
Congenital unilateral lower lip palsy(CULLP),or congenital hypoplasia of the depressor anguli oris muscle,also known as asymmetric crying facies,is a rare condition that results in asymmetry of the lower lip during sm... Congenital unilateral lower lip palsy(CULLP),or congenital hypoplasia of the depressor anguli oris muscle,also known as asymmetric crying facies,is a rare condition that results in asymmetry of the lower lip during smiling,laughing,and crying.Although the etiology is unknown,weakness of the depressor labii inferioris(DLI)muscle is implicated as a contributing factor.Currently,no well-established treatment options are available.This case report describes an 18-year-old male patient diagnosed with CULLP.Physical examination revealed a symmetric face at rest,but asymmetry when smiling and opening the mouth.Following the administration of lidocaine into the affected DLI muscle,the patient’s smile and lower lip symmetry were immediately restored without any adverse effects.Subsequently,administration of botulinum toxin for neuromodulation of the DLI muscle led to a significant improvement in symmetry and oral function within 2 weeks,which was sustained at 1 month and 3 months post-treatment.No adverse effects were reported,and both patients and families expressed high satisfaction with the outcomes.This case highlights the potential use of neuromodulation as a minimally invasive and effective treatment for CULLP. 展开更多
关键词 Facial paralysis Botulinum toxin Chemodenervation Asymmetric smile Congenital unilateral lower lip palsy Asymmetric crying facies
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Response of basement wall in tall buildings foundation under lateral loading 认领 引用
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作者 Irfan JAMIL Irshad AHMAD +3 位作者 Aqeel Ur REHMAN Aqib AHMED Ameer HAMZA Wali ULLAH 《Frontiers of Structural and Civil Engineering》 SCIE EI CSCD 2022年第11期1415-1423,共9页
With increasing population and limitation of availability of land,tall buildings supported on piled raft foundations are increasingly used in the modern world.To increase the ratio of floor area to height,and to fulfi... With increasing population and limitation of availability of land,tall buildings supported on piled raft foundations are increasingly used in the modern world.To increase the ratio of floor area to height,and to fulfill storage and parking facilities requirements,these tall buildings usually have more than one basement level.Conventionally,during the foundation design,engineers have not considered the basement wall contribution to resisting lateral load induced by earthquake or wind and this can result in an uneconomical construction of foundations.In this research work,an experimental study was performed on small-scale models,in order to study basement wall contribution,and the raft contribution including for piled raft foundations,to resisting lateral load.Three configurations of piles in 2×2,2×3,and 3×3 patterns were tested as a pile group,piled raft and piled raft with a basement wall.Results show that when a basement wall is present,the lateral displacement decreases and the demand on each pile decreases.The piled raft design can become more economical for tall buildings if the basement’s walls are taken into account. 展开更多
关键词 basement wall lateral load displacement piled raft
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