Míreanna comhchosúla: DEVELOPMENT OF A CLINICAL DIAGNOSIS AND DECISION SUPPORT SYSTEM FOR CHEST RADIOGRAPHY USING CNN /
- DEVELOPMENT OF A CLINICAL DIAGNOSIS AND DECISION SUPPORT SYSTEM FOR CHEST RADIOGRAPHY USING CNN
- QUANTIFICATION OF PULMONARY EDEMA FROM CHEST RADIOGRAPHS USING DEEP CNN /
- ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK /
- DEVELOPMENT AND ASSESSMENT OF CARBOXYMETHYL CELLULOSE LOADED ZINC OXIDE NANOPARTICLES FOR ANTIBACTERIAL PROPERTIES /
- DEVELOPMENT OF S COST-EFFECTIVE AND PORTABLE COLORIMETER DEVICE /
- Decision support systems /
Topaic: Biomedical Engineering
- Biomedical instrumentation and measurements /
- Biomedical ethics for engineers : ethics and decision making in biomedical and biosystem engineering /
- Biomedical instrumentation and measurements /
- A STUDY AND REVIEWER OPINION ON DIFFERENT IMAGING MODALITIES USED IN HDRBRACHYTHERAPY FOR PROSTATE /
- HEMODYNAMIC- STRUCTURAL COUPLED ASSESSMENT OF FLOW DIVERSION STRATEGIES IN CEREBRAL ANEURYSM /
- DEVELOPMENT AND EVALUATION OF ANTIMICROBIAL PEPTIDE (amp) INCORPORATED BIOPOLYMER BASED TOUGH ADHESIVE FOR SUTURELESS SURGERY/
Topaic: This Thesis Paper of BME in M.Sc. Program.
- DEVELOPMENT OF CHICKEN EGG WHITE AND MUPIROCIN LOADED HYDROGEL DRESSING MATERIAL FOR INHIBITION OF BACTERIAL GROWTH /
- DEVELOPMENT OF A CLINICAL DIAGNOSIS AND DECISION SUPPORT SYSTEM FOR CHEST RADIOGRAPHY USING CNN /
- ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK /
- DEVELOPMENT OF CERAMIDE AND HONEY BASED BIODEGRADABLE DRESSING MATERIALS FOR THE APPLICATION TO BURN SKIN /
- PREDICTING THE DEPTH OF ANESTHESIA FOR OPERATING PATIENT USING MUSIC-BASED SPECTRAL FEATURES OF EEG SIGNALS /
Údar: Magar, Bipin Thapa
Údar: Supervised by Asst. Prof. Dr. Md. Asadur Rahman
- DEVELOPMENT OF A CLINICAL DIAGNOSIS AND DECISION SUPPORT SYSTEM FOR CHEST RADIOGRAPHY USING CNN /
- ATTEMPTED MOVEMENT CLASSIFICATION OF SPINAL CORD INJURED PATIENTS COMBINING CNN AND LSTM NETWORK /
- PREDICTING THE DEPTH OF ANESTHESIA FOR OPERATING PATIENT USING MUSIC-BASED SPECTRAL FEATURES OF EEG SIGNALS /