Deep Learning-Based Classification of TUNEL-Detected Apoptotic Brain Damage in Light Microscopy Images at Different Electromagnetic Field Frequencies
Applied Sciences (Switzerland), cilt.16, sa.12, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 12
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/app16125889
- Dergi Adı: Applied Sciences (Switzerland)
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, INSPEC, Directory of Open Access Journals
- Anahtar Kelimeler: apoptosis, brain tissue, convolutional neural networks, deep learning, electromagnetic field exposure, histopathological image analysis, TUNEL assay
- Süleyman Demirel Üniversitesi Adresli: Evet
Özet
Exposure to electromagnetic fields (EMFs) at different frequencies has been reported to induce apoptotic changes in brain tissue. Apoptosis is commonly evaluated using the TUNEL (terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling) method; however, conventional semi-quantitative scoring is subjective and may vary between observers. Therefore, this study aimed to evaluate the feasibility of transfer learning-based convolutional neural network (CNN) models for the objective and automated classification of apoptotic damage in TUNEL-stained brain sections. A total of 92 light microscopy images of TUNEL-stained rat brain tissue, obtained from experimental groups, were analyzed. Apoptotic damage was categorized into three classes (0: no, +1: slight, +2: moderate) based on semi-quantitative scoring. Pre-trained convolutional neural network models, including AlexNet, SqueezeNet, GoogLeNet, Inception-v3, and ResNet-101, were applied for image classification. All models were able to classify apoptotic damage levels, defined by the extent of TUNEL staining, from images with varying performance. The best-performing model achieved high classification accuracy and demonstrated strong agreement with manual scoring, as determined by visual assessments by experts. The models successfully distinguished between different levels of apoptotic damage observed across experimental groups. The findings suggest that transfer learning-based CNN models may provide an objective and reproducible approach for the classification of apoptotic damage in TUNEL-stained histopathological images, thereby reducing observer-dependent variability. This approach may also support histopathological evaluation in experimental models, including studies investigating EMF-induced brain injury.