Malaria parasite detection using deep learning algorithms
| dc.contributor.advisor | Ibrahim, Abdullahi Abdu | |
| dc.contributor.author | Alnussairi, Muqdad Hanoon Dawood | |
| dc.date.accessioned | 2023-12-21T13:48:46Z | |
| dc.date.available | 2023-12-21T13:48:46Z | |
| dc.date.issued | 2023 | en_US |
| dc.date.submitted | 2023 | |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.description.abstract | The most astounding achievements in domains as disparate as robotics, NLP, image recognition, AI, and so on are the result of deep learning, an automated learning technique. one of the main benefits of deep learning is that the model only needs to be trained and taught once, in contrast to machine learning. Training and teaching in machine learning must be redone whenever the input changes. Red blood cell (RBC) examination under a microscope is the standard method for detecting malaria, which affects millions of people worldwide. Manual microscopic malaria diagnosis is difficult due to time restrictions and a lack of expertise. Using a data-driven approach to education and training, we are able to assess whether or not a person in a photograph is suffering from malaria. This proposed system aids medical students, physicians, and others in diagnosing malaria. we are working on a way for Giemsa-stained smears to detect parasites in the blood, such as those that cause malaria. When the sample size is small, deep learning algorithms perform poorly. Transfer learning means getting visual features from large general datasets and using limited problemspecific datasets to solve classification problems that are specific to that problem. in this study, we use transfer learning to find and sort malaria parasites. we employ three commonly used pre-trained CNN models: VGG19, ResNet50, and MobileNetV2. the NIH Malaria Dataset is used in all experimental evaluations, and the algorithm is found to be 100% accurate in detecting malaria. | en_US |
| dc.identifier.citation | Alnussairi, M. H. D. (2023). Malaria parasite detection using deep learning algorithms. (Yayınlanmamış doktora tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/4419 | |
| dc.identifier.yoktezid | 826449 | |
| dc.institutionauthor | Alnussairi, Muqdad Hanoon Dawood | |
| dc.language.iso | en | |
| dc.publisher | Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü | en_US |
| dc.relation.publicationcategory | Tez | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | CNN | en_US |
| dc.subject | Malaria Diagnosis | en_US |
| dc.subject | Malaria Parasite | en_US |
| dc.title | Malaria parasite detection using deep learning algorithms | |
| dc.type | Doctoral Thesis |
