A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI

A decentralized learning-based approach to classify colorectal cancer using Deep Learning Leveraging XAI

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Библиографические подробности
Главные авторы:	Mubin, Kazi Ehsanul, Arthi, Noshin Tabassum, Rahman, Junayed, Rafi, G. M., Sheja, Tahsina Tanzim
Другие авторы:	Alam, Md. Ashraful
Формат:	Диссертация
Язык:	en_US
Опубликовано:	Brac University 2022
Предметы:	Federated Learning XAI Deep Learning Colorectal Cancer Convolutional Neural Network Image Classification ResNeXt50 Artificial intelligence Machine learning
Online-ссылка:	http://hdl.handle.net/10361/17630

Схожие документы

Classification and Explanation of Different Internet of Things (IoT) Network Attacks using Machine Learning, Deep Learning and XAI
по: Tasnim, Anika, и др.
Опубликовано: (2022)

Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
по: Ifty, Md. Hasin Sarwar, и др.
Опубликовано: (2024)

Multi-classification Network for Detecting Skin Diseases using Deep Learning and XAI
по: Athina, Fahima Hasan, и др.
Опубликовано: (2022)

Interpretable MOOC dropout prediction using different Ensemble Methods and XAI
по: Khan, Labib Hasan, и др.
Опубликовано: (2023)

Heart attack prediction using machine learning and XAI
по: Ahsan, Mumtahina
Опубликовано: (2023)

Classifying insect pests from image data using deep learning
по: Mohsin, Md. Raiyan Bin, и др.
Опубликовано: (2022)

Explainable AI (XAI) driven skin cancer detection using transformer and CNN based architecture
по: Radiah, Faiza, и др.
Опубликовано: (2024)

Exploring the intersection of machine learning and explainable artificial intelligence: An analysis and validation of ML models through XAI for intrusion detection
по: Rahman, Masroor, и др.
Опубликовано: (2023)

An efficient deep learning approach to classify white blood cells
по: Turja, Afif Ibna Kadir Khan, и др.
Опубликовано: (2024)

Real-time mastitis detection in livestock using deep learning and machine learning leveraging edge devices
по: Ghosh, Kawshik Kumar, и др.
Опубликовано: (2023)

Bank loan prediction using machine learning
по: Mahottam, Parthosarothi, и др.
Опубликовано: (2023)

Unmasking malignancy of lung nodule using a modernized ConvNet toward the design of a vision transformer
по: Reza, Jamil Ur, и др.
Опубликовано: (2023)

The role of SIRT6 in down regulation of colorectal cancer
по: Hridita, Rubya Tasnim
Опубликовано: (2020)

Decipherable classification of glaucoma using deep neural network leveraging XAI
по: Chayan, Touhidul Islam, и др.
Опубликовано: (2023)

Leveraging deep learning algorithms for the timely detection of diseases in bean leaves
по: Fiona, Driciru, и др.
Опубликовано: (2024)

Essentials of learning : the new cognitive learning for students of education /
по: Travers, Robert Morris William, 1913-

A secured federated learning system leveraging confidence score to identify retinal disease
по: Eshan, M Sakib Osman, и др.
Опубликовано: (2023)

Leveraging sequential deep learning models for detecting multitude of human action categories
по: Pranta, Kazi Al Refat, и др.
Опубликовано: (2024)

Learning theories in childhood /
по: Gray, Colette, и др.
Опубликовано: (2015)

How we learn and why we don't : student survival guide using the cognitive profile inventory /
по: Krause, Lois Breur
Опубликовано: (2008)

Obscure face recognition using deep neural networks
по: Akash, MD Shahadat Hossain, и др.
Опубликовано: (2023)

Repurposing Albendazole and Mebendazole, an antiparasitic drug in treating colorectal cancer- a review
по: Mondal, Shawon
Опубликовано: (2023)

An interpretable transformer based approach to classify Malaria from blood cell images
по: Islam, Mehafuza, и др.
Опубликовано: (2023)

Internship report on micro-learning blessing for generation Z
по: Uddin, Junayed
Опубликовано: (2024)

Quantum lattice learning and explainable artificial intelligence for maternal and child healthcare
по: Marvin, Ggaliwango
Опубликовано: (2022)

Self-learning game bot using deep reinforcement learning
по: Ananto, Azizul Haque
Опубликовано: (2018)

Development of mobile learning system based on android app
по: Samrin, Sama, и др.
Опубликовано: (2018)

Analysis of the impact of online education using EEG signals and machine learning algorithms
по: Hoque, Ehsanul, и др.
Опубликовано: (2021)

Mastery learning: theory and practice.
Опубликовано: (1971)

The conditions of learning /
по: Gagné, Robert M. (Robert Mills), 1916-2002
Опубликовано: (1977)

Learning in adulthood : a comprehensive guide /
по: Merriam, Sharan B.
Опубликовано: (1991)

Essentials of learning for instruction /
по: Gagné, Robert M. (Robert Mills), 1916-2002
Опубликовано: (1988)

FakeDetect: Bangla fake news detection model based on different machine learning classifiers
по: Sraboni, Tasnuba, и др.
Опубликовано: (2021)

Explainable artificial intelligence and model calibration for water quality prediction
по: Hellen, Nakayiza
Опубликовано: (2022)

A machine learning approach on classifying orthopedic patients based on their biomechanical features
по: Hasan, Kamrul, и др.
Опубликовано: (2018)

Learning theories for teachers /
по: Bigge, Morris L.
Опубликовано: (1982)

Understanding learning : influences and outcomes /
Опубликовано: (2001)

An autoencoder-based decentralized clustering leveraging model aggregation fusion strategy
по: Hasan, Nahid
Опубликовано: (2024)

Prostate cancer detection using deep learning neural network with transfer learning approach
по: Badhon, Ariful Islam Mahmud, и др.
Опубликовано: (2022)

How people learn : brain, mind, experience, and school /
Опубликовано: (2000)