Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification

Deep learning-based hybrid multi-task model for adrenocortical carcinoma segmentation and classification

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.

Bibliographische Detailangaben
Hauptverfasser:	Datta, Nirjhor, Rashid, Md. Hasanur, Rahman, Samiur, Nodi, Naima Tahsin, Uddin, Moin
Weitere Verfasser:	Hossain, Muhammad Iqbal
Format:	Abschlussarbeit
Sprache:	English
Veröffentlicht:	Brac University 2024
Schlagworte:	Deep learning Convolutional neural network CNN Adrenocortical Carcinoma Disease detection Neural networks (Computer science) Deep learning (Machine learning) Computational intelligence
Online Zugang:	http://hdl.handle.net/10361/22838

Ähnliche Einträge

Classification of peripheral blood cell images using deep learning
von: Aadi, Oyshik Ahmed, et al.
Veröffentlicht: (2024)

Visual object classification from fMRI data
von: Newaz, Syed Mishar, et al.
Veröffentlicht: (2022)

ProteoKnight: phage virion protein classification with CNN and uncertainty quantification
von: Bhuiyan, Abir Ahammed, et al.
Veröffentlicht: (2024)

Diabetic retinopathy detection and classification by using deep learning
von: Hossain, Shahriar, et al.
Veröffentlicht: (2022)

Analysis of transformer and CNN based approaches for classifying renal abnormality from image data
von: Reza, S. M. Mushfiq, et al.
Veröffentlicht: (2024)

An efficient deep learning approach to detect skin Cancer
von: Islam, Ashfaqul, et al.
Veröffentlicht: (2022)

Cancer classification using deep learning from medical image data
von: Monir, Raiyan Janik, et al.
Veröffentlicht: (2022)

Skin cancer detection and classification using multiple optimized deep convolutional neural network
von: Sakir, Adnan, et al.
Veröffentlicht: (2023)

Bangali Handwritten characters classification using Deep Convolutional Neural Network
von: Sikder, Shihab Uddin, et al.
Veröffentlicht: (2022)

Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
von: Ifty, Md. Hasin Sarwar, et al.
Veröffentlicht: (2024)

Analysis of uncertainty in different neural network structures using monte carlo dropout
von: Islam, Md. Farhadul, et al.
Veröffentlicht: (2024)

An efficient deep learning approach to detect skin cancer using image data
von: Chowdhury, Yaser Al Rahman, et al.
Veröffentlicht: (2023)

Intracranial hemorrhage detection using CNN-LSTM fusion model
von: Ahmed, Kazi sabab, et al.
Veröffentlicht: (2022)

An efficient ML approach to detect brain tumor using MRI images
von: Muktadir, MD. Arafat, et al.
Veröffentlicht: (2024)

Classification of respiratory diseases and COVID-19 from respiratory and cough sound using deep learning techniques
von: Ahasan, Md. Mubtasim, et al.
Veröffentlicht: (2022)

Early Schizophrenia Diagnosis with 3D Convolutional Neural Network
von: Ashraf, S.M. Nabil, et al.
Veröffentlicht: (2021)

Analysis of uncertainty in different neural network structures using Monte Carlo Dropout
von: Islam, Md. Farhadul, et al.
Veröffentlicht: (2024)

Corn leaf disease detection using deep convolution neural network
von: Rabbi, Rawhatur, et al.
Veröffentlicht: (2023)

Visual object recognition using deep convolutional neural network
von: Ahmed, Sabbir
Veröffentlicht: (2017)

Automatic brain tumor segmentation using U-ResUNet chain model approach
von: Alam, Mohd Tanjeem, et al.
Veröffentlicht: (2022)

An efficient deep learning approach to classify white blood cells
von: Turja, Afif Ibna Kadir Khan, et al.
Veröffentlicht: (2024)

Classification of retinal diseases from OCT images using deep learning models
von: Mokarrom, Md Saif, et al.
Veröffentlicht: (2024)

Classification of different magnetic structures from image data using deep neural networks
von: Ibn Hasib, Fahad, et al.
Veröffentlicht: (2023)

Plant leaf disease identification
von: Hasan Mahin, Mohammad Rakibul, et al.
Veröffentlicht: (2024)

Bang-lish sentiment classification using deep learning
von: Saleheen, Abrar, et al.
Veröffentlicht: (2023)

Efficient image processing and machine learning approach for predicting retinal diseases
von: Hasib, Mehadi Hasan, et al.
Veröffentlicht: (2021)

Detection and classification of Mango Leaf diseases utilizing convolutional neural network models
von: Ferdous, Md.Rubaiat, et al.
Veröffentlicht: (2024)

X-Ray classification to detect COVID-19 using ensemble model
von: Solaiman, Ishmam Ahmed, et al.
Veröffentlicht: (2021)

Comparative analysis and implementation of credit risk prediction through distinct machine learning models
von: Turjo, Aquib Abtahi, et al.
Veröffentlicht: (2021)

A conventional & deep learning strategy for analyzing & detecting Bengali fake news in online medium
von: Ahmed, Istiak, et al.
Veröffentlicht: (2023)

An eﬃcient deep learning approach for detecting lung disease from chest X-ray images using transfer learning and ensemble modeling
von: Sagor, Mostofa Kamal, et al.
Veröffentlicht: (2021)

BanglaBait: using transformers, neural networks & statistical classifiers to detect clickbaits in New Bangla Clickbait Dataset
von: Mahtab, Motahar, et al.
Veröffentlicht: (2022)

Detection of handwritten text using convolutional neural network
von: Jasim, Rabib Bin, et al.
Veröffentlicht: (2024)

Hybrid recommendation system of intelligent captioning using deep learning networks
von: Wasi, Ahamed Al, et al.
Veröffentlicht: (2024)

Real time action recognition from video footage
von: Apon, Tasnim Sakib, et al.
Veröffentlicht: (2021)

Cassava leaf disease classification using deep learning and convolutional neural network ensemble
von: Shahriar, Hasan, et al.
Veröffentlicht: (2022)

Recall-Net: A CNN-based Model for Four-class Classification of Alzheimer’s Disease
von: Hasan, M. M. Kamrul, et al.
Veröffentlicht: (2022)

Leveraging sequential deep learning models for detecting multitude of human action categories
von: Pranta, Kazi Al Refat, et al.
Veröffentlicht: (2024)

Autonomous fault diagnosis of commercially available PV modules using high-end deep learning frameworks
von: Tasawar, Ihtyaz Kader, et al.
Veröffentlicht: (2021)

Prostate cancer detection using deep learning neural network with transfer learning approach
von: Badhon, Ariful Islam Mahmud, et al.
Veröffentlicht: (2022)