Akbacak, Enver

Profile URL
https://hdl.handle.net/20.500.14627/501
Name Variants
Akbacak, Enver Akbacak, E.
Job Title
Doktor Öğretim
Email Address
enver.akbacak@fbu.edu.tr
Main Affiliation
Bilgisayar Mühendisliği Bölümü
Status
Former Staff
Website
ORCID ID
0000-0002-6753-7887
Scopus Author ID
57218549439
Turkish CoHE Profile ID
Google Scholar ID
Q-exyJIAAAAJ
WoS Researcher ID
AAA-7122-2021
Files
photo_335124.jpg.JPG (2.29 MB)

Research Topics

Domains
Physical Sciences
Fields
Computer Science
Subfields
Computer Vision and Pattern Recognition
Specific Research Areas
Advanced Image and Video Retrieval Techniques
Image Retrieval and Classification Techniques
Video Analysis and Summarization
Multimodal Machine Learning Applications
Advanced Steganography and Watermarking Techniques

Sustainable Development Goals

SDG data is not available

Publication Collaboration

Affiliation Name Count
Haliç University 6
Marmara University 4
Fenerbahçe University 3
Karamanoğlu Mehmetbey University 3
Çanakkale Onsekiz Mart Üniversitesi 2
1 / 2
Data obtained from OpenAlex
Scholarly Output

3

Articles

2

WoS Citation Count

4

Scopus Citation Count

7

Filters

20243

Settings

Author of Publication: search.filters.isAuthorOfPublication.5475b6ea-6aa9-4763-ba07-dba75c8f8e9d

Scholarly Output Search Results

Now showing 1 - 3 of 3
  • Article
    Ensemble-Based Alzheimer's Disease Classification Using Features Extracted From Hog Descriptor and Pre-Trained Models
    (Sakarya University, 2024) Muzoğlu, Nedim; Akbacak, Enver
    Alzheimer's Disease is the most common type of dementia and is a progressive, neurodegenerative disease. The disease worsens over time, and the patient becomes bedridden, unable to move or understand what is happening around him. The main concern of medicine is to slow down the progression of the disease for which no treatment has yet been developed. Artificial intelligence studies have achieved significant success in detecting many diseases. In this study, an artificial intelligence-based approach that uses MR images of the early stage of Alzheimer's Disease to detect the disease at an early stage is presented. Initially, a new dataset was created through the application of the fuzzy technique, thereby expanding the feature space. Then, an ensemble learning-based hybrid deep learning model was developed to reduce the misclassification rate for all classes. The features derived from the inception module, residual modules, and histogram of oriented gradients descriptor are subjected to classification through bagging and boosting algorithms. The proposed model has surpassed many state-of-the-art studies by achieving a high success rate of 99.60% in detecting Alzheimer's disease in its early stages.
  • Conference Object
    Citation - Scopus: 2
    Unet3D Based Next Frame Prediction;
    (Institute of Electrical and Electronics Engineers Inc., 2024) Akbacak, E.
    The concept of next-frame prediction, which is predicting the subsequent frames using historical frames' spatial and temporal properties, is indispensable in computer vision. There are various application of frame prediction such as predicting a future event in autonomous vehicles, predicting patient falls in biomedical engineering, and reducing the amount of data transmitted in video transmission. Deep learning applications in this field are the focus of the most effective methods. Especially CNN-LSTM, Convolutional LSTMs, and GAN-supported deep learning methods are very common. This study proposes the inflated 3D Unet encoder-decoder model, which is not yet used for the next-frame prediction problem. The proposed model predicts both the next frame and the subsequent frames. Experimental results have shown that the proposed method gives better results than CNN-LSTM and Convolutional LSTMs. © 2024 IEEE.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 5
    An Efficient and Robust 3d Medical Image Classification Approach Based on 3d Cnn, Time-Distributed 2d Cnn-Blstm Models, and Mrmr Feature Selection
    (Wiley, 2024) Akbacak, Enver; Muzoglu, Nedim
    The advent of 3D medical imaging has been a turning point in the diagnosis of various diseases, as voxel information from adjacent slices helps radiologists better understand complex anatomical relationships. However, the interpretation of medical images by radiologists with different levels of expertise can vary and is also time-consuming. In the last decades, artificial intelligence-based computer-aided systems have provided fast and more reliable diagnostic insights with great potential for various clinical purposes. This paper proposes a significant deep learning based 3D medical image diagnosis method. The method classifies MedMNIST3D, which consists of six 3D biomedical datasets obtained from CT, MRA, and electron microscopy modalities. The proposed method concatenates 3D image features extracted from three independent networks, a 3D CNN, and two time-distributed ResNet BLSTM structures. The ultimate discriminative features are selected via the minimum redundancy maximum relevance (mRMR) feature selection method. Those features are then classified by a neural network model. Experiments adhere to the rules of the official splits and evaluation metrics of the MedMNIST3D datasets. The results reveal that the proposed approach outperforms similar studies in terms of accuracy and AUC.