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Hamid GholamHosseini
Also published under: H. GholamHosseini, Hamid Gholamhosseini, H. Gholamhosseini
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Development of Mobile Application for Skin Cancer Detection Using Deep Learning Models
Ranpreet Kaur;Hamid GholamHosseini;Paula Iskander;Radwa Taha;Shereen Afifi
2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI)
Year: 2024 | Conference Paper |
Skin cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the critical need for accurate and timely diagnosis. The current clinical treatments are time-consuming and susceptible to human error, which can delay treatment and affect patient outcomes. This research addresses these challenges by making two major contributions to the field. Firstly, four fine-tuned deep n...Show More
Deep Learning-Powered Mobile Application For Early Skin Cancer Detection
Shereen Moataz Afifi;Ranpreet Kaur;Hamid GholamHosseini;Omar Azzam;Radwa Taha
2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI)
Year: 2024 | Conference Paper |
This research work presents the development and implementation of a skin scan app utilizing deep learning technologies. The aim is to provide users with a convenient and accessible tool for skin analysis to reduce the reliance on traditional dermatological solutions. After extensive exploration of different deep learning techniques, an efficient and accurate model, EffecientNetB0V2 was identified ...Show More
Lesion Segmentation in Skin Cancer Images using Fusion Model via Deep Learning Networks
Ranpreet Kaur;Hamid GholamHosseini;Nada Hegazy;Radwa Taha;Shereen Afifi
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2024 | Conference Paper |
Skin cancer, one of the most prevalent and life-threatening cancers globally, has become a focus of deep learning applications due to its significant impact on diagnostic accuracy. This research specifically addresses lesion segmentation in skin cancer images, recognizing its direct influence on classification precision. Six diverse deep learning models, including DeepLabV3+, EfficientNetB7, VGG19...Show More
Analyzing the Impact of Image Denoising and Segmentation on Melanoma Classification Using Convolutional Neural Networks
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2023 | Conference Paper |
Early skin cancer detection and its treatment are crucial for reducing death rates worldwide. Deep learning techniques have been used successfully to develop an automatic lesion detection system. This study explores the impact of pre-processing steps such as data augmentation, contrast enhancement, and segmentation on improving the convolutional neural network (CNN) performance for lesion classifi...Show More
Synthetic Images Generation Using Conditional Generative Adversarial Network for Skin Cancer Classification
TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)
Year: 2021 | Conference Paper |
Cited by: Papers (5)
Deep learning and computer vision have achieved remarkable success in many areas of machine learning and medical diagnostics. However, there is still a remarkable gap between dermatologists' skin cancer diagnosis and reliable computer-aided melanoma detection. There are several reasons behind this gap, and the availability of insufficient data for training deep learning networks is one of them. Da...Show More
A Usability and User-Experience Analysis of VitalsAssist: A mHealth Application to Monitor Vital Signs in Acute Care Settings
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Year: 2020 | Conference Paper |
Cited by: Papers (5)
M-health applications are playing an important role in current healthcare delivery, individual's health and well-being. Usability of mHealth applications (apps) is a critical factor for the success of the apps, yet this is often overlooked in the current health care solutions in primary care, secondary (acute) care, community care and especially in remote patient monitoring applications. This work...Show More
Automated Glaucoma Diagnosis Using Deep and Transfer Learning: Proposal of a System for Clinical Testing
Mohammad Norouzifard;Ali Nemati;Hamid GholamHosseini;Reinhard Klette;Kouros Nouri-Mahdavi;Siamak Yousefi
2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Year: 2018 | Conference Paper |
Cited by: Papers (17)
We developed a deep learning algorithm for identifying glaucoma on optic nerve head (ONH) photographs. We applied transfer learning to overcome overfitting on the small training sample size that we employed. The transfer learning framework that was previously trained on large datasets such as ImageNet, uses the initial parameters and makes the approach applicable to small sample sizes. We then cla...Show More
Superpixel Segmentation Methods on Stereo Fundus Images and Disparity Map for Glaucoma Detection
Mohammad Norouzifard;Arpita Dawda;Anmar Abdul-Rahman;Hamid GholamHosseini;Reinhard Klette
2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Glaucoma is an optic neuropathy resulting in progressive vision loss. It is the leading cause of global irreversible blindness. The reported prevalence among the population in New Zealand is 2% over the age of 40 years. About 10 % of those over 70 years are diagnosed with this disease. Population-based studies report high rates of undiagnosed glaucoma with over 50 % of the population with glaucoma...Show More
Unsupervised Optic Cup and Optic Disk Segmentation for Glaucoma Detection by ICICA
Mohammad Norouzifard;Amin Abdollahi Dehkordi;Mohammad Naderi Dehkordi;Hamid Gholamhosseini;Reinhard Klette
2018 15th International Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
Glaucoma is an eye disease that can lead to vision loss by damaging the optic nerve. Although this disease can often be prevented with early glaucoma detection, lack of discernible early symptoms makes the diagnosis difficult. Measuring the cup-to-disc ratio (CDR) is a common approach for glaucoma detection. Glaucoma can be specified by thinning the rim area that identifies the CDR value. Clusteri...Show More
Clinical Decision Support for Early Detection of Prediabetes and Type 2 Diabetes Mellitus Using Wearable Technology
Mirza Baig;Farhaan Mirza;Hamid GholamHosseini;Jairo Gutierrez;Ehsan Ullah
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2018 | Conference Paper |
Cited by: Papers (3)
Worldwide spending on long-term and chronic care conditions is increasing to a point that requires immediate interventions and advancements to reduce the burden of the healthcare cost. This research is focused on early detection of prediabetes and type 2 diabetes mellitus (T2DM) using wearable technology. An artificial intelligence model was developed based on adaptive-neuro fuzzy interference to ...Show More
Interoperability and reliability of wearable healthcare devices for emergency care
2018 IEEE International Conference on Consumer Electronics (ICCE)
Year: 2018 | Conference Paper |
Wearable consumer healthcare devices are becoming increasingly popular over recent years with a range of non-invasive sensors that continually track a user's health. These consumer wearables differ from medical devices mainly due to the fact that they do not need to undergo the stringent FDA approval process. One of the main advantages of these wearables is to provide important information about t...Show More
Intrinsic motivated cervical cancer screening intervention framework
Md. Rajib Hasan;Hamid Gholamhosseini;Nurul I Sarkar;S M Safiuzzaman
2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)
Year: 2017 | Conference Paper |
Cited by: Papers (3)
Cervical cancer turned into a reason of extreme mortality even though it is preventable. The expansion rate of cervical cancer is at alarming rate internationally, including both developed (e.g. New Zealand) and developing (e.g. Bangladesh) countries. This study considers survey data collected from Chittagong Medical College Hospital in Bangladesh and other secondary data from open sources. Studie...Show More
A new ensemble classifier for multivariate medical data
2017 27th International Telecommunication Networks and Applications Conference (ITNAC)
Year: 2017 | Conference Paper |
Cited by: Papers (8)
Selecting an efficient classifier for medical data is considered as one of the most important part of today's computer aided diagnosis. The performance of single classifiers such as decision tree classifier can be increased by ensemble method. However, this approach relies on the data quality and missing values. In this paper, we propose a new ensemble classifier to overcome overfitting and biasne...Show More
SVM classifier on chip for melanoma detection
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2017 | Conference Paper |
Cited by: Papers (22)
Support Vector Machine (SVM) is a common classifier used for efficient classification with high accuracy. SVM shows high accuracy for classifying melanoma (skin cancer) clinical images within computer-aided diagnosis systems used by skin cancer specialists to detect melanoma early and save lives. We aim to develop a medical low-cost handheld device that runs a real-time embedded SVM-based diagnosi...Show More
A low-cost FPGA-based SVM classifier for melanoma detection
2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)
Year: 2016 | Conference Paper |
Cited by: Papers (19)
Support Vector Machines (SVMs) are common machine learning tools with accurate classification. Hardware implementation of SVM classifiers for real-time applications can improve their computing performance and reduce power consumption. This study aims to develop a real-time embedded classifier to be implemented on a low-cost handheld device dedicated for early detection of melanoma. Melanoma is the...Show More
Lesion border detection using deep learning
2016 IEEE Congress on Evolutionary Computation (CEC)
Year: 2016 | Conference Paper |
Cited by: Papers (18)
Computer aided diagnosis of medical images can result in (better) detection in addition to early diagnosis of many symptoms to assist health physicians and therefore reducing the mortality rate. Realization of an efficient mobile device for automatic diagnosis of melanoma would greatly enhance the applicability of medical image classification scheme and make it useful in clinical contexts. In this...Show More
Cuff-less, non-invasive and continuous blood pressure monitoring using indirect methods
Hamid Gholamhosseini;Jit Biswas;Haihong Zhang;Maniyeri Jayachandran;Mirza Mansoor Baig
2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2015 | Conference Paper |
Cited by: Papers (1)
Cuff-based methods for measuring blood pressure (BP) have shown some limitations particularly when continuous measurement is required. Moreover, the inflation of the cuff is one of the main disturbing factors as expressed among patients. In this study, a promising approach based on changes in pulse transit time (PTT) for continuous, non-invasive and indirect measurement of BP is proposed to overco...Show More
Integrated vital signs monitoring system using ubiquitous devices: Multiple physical signs detection and decision support for hospitalized older adults
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2015 | Conference Paper |
Cited by: Papers (4)
Vital signs monitoring systems are rapidly becoming the core of today's healthcare deliveries. The paradigm has shifted from traditional and manual recording to computer based electronic records and further to handheld devices as versatile and innovative healthcare monitoring systems. Interpretation of vital signs to early detect multiple physical signs using a multifactorial and holistic approach...Show More
Tablet-based patient monitoring and decision support systems in hospital care
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Year: 2015 | Conference Paper |
Cited by: Papers (5)
Remote patient monitoring with evidence-based decision support is revolutionizing healthcare. This novel approach could enable both patients and healthcare providers to improve quality of care and reduce costs. Clinicians can also view patients' data within the hospital network on tablet computers as well as other ubiquitous devices. Today, a wide range of applications are available on tablet comp...Show More
A cascade classifier for diagnosis of melanoma in clinical images
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2014 | Conference Paper |
Cited by: Papers (23)
Computer aided diagnosis of medical images can help physicians in better detecting and early diagnosis of many symptoms and therefore reducing the mortality rate. Realization of an efficient mobile device for semi-automatic diagnosis of melanoma would greatly enhance the applicability of medical image classification scheme and make it useful in clinical contexts. In this paper, interactive object ...Show More
A multifactorial falls risk prediction model for hospitalized older adults
Hamid GholamHosseini;Mirza Mansoor Baig;Martin J. Connolly;Maria Lindén
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Year: 2014 | Conference Paper |
Cited by: Papers (12)
Ageing population worldwide has grown fast with more cases of chronic illnesses and co-morbidity, involving higher healthcare costs. Falls are one of the leading causes of unintentional injury-related deaths in older adults. The aim of this study was to develop a robust multifactorial model toward the falls risk prediction. The proposed model employs real-time vital signs, motion data, falls histo...Show More
Fault diagnosis in a distributed motor network using Artificial Neural Network
2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion
Year: 2014 | Conference Paper |
Cited by: Papers (12)
Signature analysis methods have been proven to deliver good results in the laboratory environment and successfully applied to isolated motors. The influence of fault signal on a non-faulty motor may be interpreted as faulty condition of the healthy motor. Therefore, it is difficult to identify a motor fault within a network and precisely identify the type of fault. This paper presents a supervised...Show More
Real-time vital signs monitoring and interpretation system for early detection of multiple physical signs in older adults
Mirza Mansoor Baig;Hamid GholamHosseini;Martin J. Connolly;Ghodsi Kashfi
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
Year: 2014 | Conference Paper |
Cited by: Papers (12)
Advanced engineering, communication and information technologies combined with medical and clinical knowledge enable the possibility of remote, wireless, continuous physiological monitoring. These technologies facilitate the implementation of patient monitoring and diagnostic systems virtually anywhere: home, hospital and outdoors (on the move). Physiological parameters are considered as critical ...Show More
A wireless patient monitoring system for hospitalized older adults: Acceptability, reliability and accuracy evaluation
Mirza Mansoor Baig;Hamid GholamHosseini;Martin J. Connolly;Ghodsi Kashfi
IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
Year: 2014 | Conference Paper |
Cited by: Papers (5)
Wireless patient monitoring systems are becoming increasing acceptable in today's healthcare market, because of their low cost and easy adoption/integration features. However, there is little research on assessing the clinical acceptability, accuracy and reliability of such systems. This paper aims to address some of the current issues and challenges facing wireless monitoring systems by adopting ...Show More
Biotronic Engineering curriculum design: Integrating electronic engineering and applied/health sciences
Proceedings of 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)
Year: 2013 | Conference Paper |
A specialised major in Biotronic Engineering that meets the needs of health care industry was offered at Auckland University of Technology (AUT). The Biotronic Engineering major focuses on integrating electronic and computer systems engineering with human biology and applied sciences to solve problems related to medical systems and devices. Moreover, it covers the design, build and maintenance of ...Show More
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