Myocardial infarction detection using ECG signal applying deep learning techniques - ConvNet, VGG16, InceptionV3 and MobileNet
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
Egile Nagusiak: | Promita, Samanta Tabassum, Biswas, Simon Abhijet, Mozumder, Nisat Islam, Taharat, Mamur |
---|---|
Beste egile batzuk: | Uddin, Jia |
Formatua: | Thesis |
Hizkuntza: | English |
Argitaratua: |
Brac University
2022
|
Gaiak: | |
Sarrera elektronikoa: | http://hdl.handle.net/10361/16812 |
Antzeko izenburuak
-
Tomato leaf disease detection using Resnet-50 and MobileNet Architecture
nork: Tahamid, Abu
Argitaratua: (2021) -
Plant disease diagnosis using deep transfer learning architectures- VGG19, MobileNetV2 and Inception-V3
nork: Kobra, Khadija-Tul, et al.
Argitaratua: (2022) -
A comparison of deep learning U‐Net architectures for semantic segmentation on panoramic X-ray images
nork: Bin Mushfiq, Rahil, et al.
Argitaratua: (2024) -
Image segmentation of X-Ray and optical images using U-Net/UNet++ based deep learning architecture
nork: Sharma, Tanmoyee, et al.
Argitaratua: (2021) -
Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI
nork: Athina, Fahima Hasan, et al.
Argitaratua: (2022)