http://hdl.handle.net/20.500.12566/16 2026-04-06T01:16:55Z 2026-04-06T01:16:55Z Taheri, Shahram Golrizkhatami, Zahra http://hdl.handle.net/20.500.12566/2362 2025-12-02T09:57:43Z 2023-01-01T00:00:00Z

Magnification-specific and magnification-independent classification of breast cancer histopathological image using deep learning approaches Taheri, Shahram; Golrizkhatami, Zahra Breast cancer (BC) is a massive health problem and a deadly disease, killing millions of people every year. Computerized approaches for automated malignant BC detection can efficiently help in reducing the manual workload of pathologists and making diagnosis more scalable and less prone to errors. In this paper, we present two systems to diagnose breast cancer from single and multi-magnification histopathological images. The first proposed system utilizes a pre-trained DenseNet201 CNN architecture and fine-tuned over the publicly available BreakHis dataset and classifies histopathological images of specific magnification factors into one of the benign or malignant classes. The second system consists of four subsystems, each corresponding to one of the magnifications, and is trained only by related magnification images. Afterwards, the results obtained from these four subsystems are fused together to make the final decision. Several experiments on BreakHis dataset demonstrate that the proposed systems outperform the state-of-the-art approaches, in all cases.

2023-01-01T00:00:00Z Taheri, Shahram Golrizkhatami, Zahra Sivri, Mustan Barış Kırzıoğlu Ercan, Rukiye Gözde Yağcı, Ünsun http://hdl.handle.net/20.500.12566/2361 2025-12-02T09:56:25Z 2024-01-01T00:00:00Z

Dental age estimation: A comparative study of convolutional neural network and Demirjian's method Taheri, Shahram; Golrizkhatami, Zahra; Sivri, Mustan Barış; Kırzıoğlu Ercan, Rukiye Gözde; Yağcı, Ünsun The aim of this study is to compare a technique using Convolutional Neural Network (CNN) with the Demirjian's method for chronological age estimation of living individuals based on tooth age from panoramic radiographs. This research used 5898 panoramic X-ray images collected for diagnostic from pediatric patients aged 4–17 who sought treatment at Antalya Oral and Dental Health Hospital between 2015 and 2020. The Demirjian's method's grading was executed by researchers who possessed appropriate training and experience. In the CNN method, various CNN architectures including Alexnet, VGG16, ResNet152, DenseNet201, InceptionV3, Xception, NASNetLarge, InceptionResNetV2, and MobieNetV2 have been evaluated. Densenet201 exhibited the lowest MAE value of 0.73 years, emphasizing its superior accuracy in age estimation compared to other architectures. In most age categories, the predicted age closely matches the actual age. The most inconsistent results are observed at ages 12 and 13. The results highlight correspondence between the age predicted by CNN and the Demirjian's approach. In conclusion, the results show that the CNN method is adequate to be an alternative to the Demirjian's age estimation method. We suggest that convolutional neural network can effectively optimize the accuracy of age estimation and can be faster than traditional methods, eliminating the need for additional learning from experts.

2024-01-01T00:00:00Z Taheri, Shahram Golrizkhatami, Zahra Basabrain, Ammar A. Hazzazi, Mohannad S http://hdl.handle.net/20.500.12566/2360 2025-12-02T09:54:01Z 2024-01-01T00:00:00Z

A comprehensive study on classification of breast cancer histopathological images: binary versus multi-category and magnification-specific versus magnification-independent Taheri, Shahram; Golrizkhatami, Zahra; Basabrain, Ammar A.; Hazzazi, Mohannad S There are millions of cancer cases worldwide every year, and breast cancer is one of the most prevalent diseases with the highest mortality rate. The manual effort of pathologists can be significantly reduced by computerized diagnostic systems, which improve the accuracy and reliability of diagnosis. In this paper, we present four novel systems for breast cancer diagnosis in four different scenarios: binary versus multi-class classification and magnification-specific (MS) versus magnification-independent (MI) classification. In each of the proposed systems, we developed an automatic score-level fused CNN model using a pretrained deep neural network and named it the Multi-Level Feature Fusion (MLF2) model. The MLF2-CNN, similar to the conventional CNN models, integrates the feature extraction and classification phases of BC classification into a single automatic learning procedure. Additionally, MLF2-CNN performs an automatic score-level fusion of several classifiers that were trained with multi-level features to make the final decision. A pretrained DenseNet-121 is selected as the backbone of the proposed MLF2-CNN, and several new links are added to the CNN architecture to capture multi-stage features. Several experiments on the publicly available BreakHis dataset demonstrate that the proposed systems capture the best descriptive features and outperform state-of-the-art techniques in most of the scenarios.

2024-01-01T00:00:00Z Kolaj, Adelaida Zahr, Siraj K. Wang, Beatrix S. Krawec, Taylor Kazan, Hilal Yang, Guang Kaplan, David R. Miller, Freda D. http://hdl.handle.net/20.500.12566/2119 2024-04-17T11:30:07Z 2023-01-01T00:00:00Z

The P-body protein 4E-T represses translation to regulate the balance between cell genesis and establishment of the postnatal NSC pool Kolaj, Adelaida; Zahr, Siraj K.; Wang, Beatrix S.; Krawec, Taylor; Kazan, Hilal; Yang, Guang; Kaplan, David R.; Miller, Freda D. Here, we ask how developing precursors maintain the balance between cell genesis for tissue growth and establishment of adult stem cell pools, focusing on postnatal forebrain neural precursor cells (NPCs). We show that these NPCs are transcriptionally primed to differentiate and that the primed mRNAs are associated with the translational repressor 4E-T. 4E-T also broadly associates with other NPC mRNAs encoding transcriptional regulators, and these are preferentially depleted from ribosomes, consistent with repression. By contrast, a second translational regulator, Cpeb4, associates with diverse target mRNAs that are largely ribosome associated. The 4E-T-dependent mRNA association is functionally important because 4E-T knockdown or conditional knockout derepresses proneurogenic mRNA translation and perturbs maintenance versus differentiation of early postnatal NPCs in culture and in vivo. Thus, early postnatal NPCs are primed to differentiate, and 4E-T regulates the balance between cell genesis and stem cell expansion by sequestering and repressing mRNAs encoding transcriptional regulators.

2023-01-01T00:00:00Z