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Firas Alghanim, Ibrahim Al-Hurani, Hazem Qattous, Abdullah Al-Refai, Osamah Batiha, Abedalrhman Alkhateeb and Salama Ikki
Identifying menopause-related breast cancer biomarkers is crucial for enhancing diagnosis, prognosis, and personalized treatment at that stage of the patient?s life. In this paper, we present a comprehensive framework for extracting multiomics biomarkers...
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Armin Soltan and Peter Washington
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Flavia Grignaffini, Maurizio Troiano, Francesco Barbuto, Patrizio Simeoni, Fabio Mangini, Gabriele D?Andrea, Lorenzo Piazzo, Carmen Cantisani, Noah Musolff, Costantino Ricciuti and Fabrizio Frezza
Skin cancer (SC) is one of the most common cancers in the world and is a leading cause of death in humans. Melanoma (M) is the most aggressive form of skin cancer and has an increasing incidence rate. Early and accurate diagnosis of M is critical to incr...
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Pedro F. Durães and Mário P. Véstias
The very good results achieved with recent algorithms for image classification based on deep learning have enabled new applications in many domains. The medical field is one that can greatly benefit from these algorithms in order to help the medical prof...
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Mohammad H. Alshayeji and Jassim Al-Buloushi
Improved disease prediction accuracy and reliability are the main concerns in the development of models for the medical field. This study examined methods for increasing classification accuracy and proposed a precise and reliable framework for categorizi...
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Jöran Rixen, Nico Blass, Simon Lyra and Steffen Leonhardt
Breast cancer is the leading cause of cancer-related death among women. Early prediction is crucial as it severely increases the survival rate. Although classical X-ray mammography is an established technique for screening, many eligible women do not con...
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Zahra Jafari and Ebrahim Karami
The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extracti...
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Catur Supriyanto, Abu Salam, Junta Zeniarja and Adi Wijaya
This research paper presents a deep-learning approach to early detection of skin cancer using image augmentation techniques. We introduce a two-stage image augmentation process utilizing geometric augmentation and a generative adversarial network (GAN) t...
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Carlos Macancela, Manuel Eugenio Morocho-Cayamcela and Oscar Chang
In August 2020, the World Health Assembly launched a global initiative to eliminate cervical cancer by 2030, setting three primary targets. One key goal is to achieve a 70% screening coverage rate for cervical cancer, primarily relying on the precise ana...
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Jie Xu, Xing He, Wei Shao, Jiang Bian and Russell Terry
Up to 20% of renal masses =4 cm is found to be benign at the time of surgical excision, raising concern for overtreatment. However, the risk of malignancy is currently unable to be accurately predicted prior to surgery using imaging alone. The objective ...
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M. F. Mridha, Md. Kishor Morol, Md. Asraf Ali, MD SAKIB HOSSAIN SHOVON
Pág. 53 - 81
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Sufiyan Bashir Mukadam and Hemprasad Yashwant Patil
Skin cancer is one of the most fatal diseases for mankind. The early detection of skin cancer will facilitate its overall treatment and contribute towards lowering the mortalities. This paper presents the deep learning-based algorithm along with pre-proc...
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Jacinth Poornima Jeyakumar, Anitha Jude, Asha Gnana Priya and Jude Hemanth
Melanoma is one of the skin cancer types that is more dangerous to human society. It easily spreads to other parts of the human body. An early diagnosis is necessary for a higher survival rate. Computer-aided diagnosis (CAD) is suitable for providing pre...
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David Clement, Emmanuel Agu, John Obayemi, Steve Adeshina and Wole Soboyejo
Breast cancer accounts for 30% of all female cancers. Accurately distinguishing dangerous malignant tumors from benign harmless ones is key to ensuring patients receive lifesaving treatments on time. However, as doctors currently do not identify 10% to 3...
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Alireza Rezazadeh, Yasamin Jafarian and Ali Kord
Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in p...
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Flavia Grignaffini, Francesco Barbuto, Lorenzo Piazzo, Maurizio Troiano, Patrizio Simeoni, Fabio Mangini, Giovanni Pellacani, Carmen Cantisani and Fabrizio Frezza
Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate ...
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James Barrett and Thiago Viana
Lung cancer (LC) is the most common cause of cancer-related deaths in the UK due to delayed diagnosis. The existing literature establishes a variety of factors which contribute to this, including the misjudgement of anatomical structure by doctors and ra...
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Khalil Aljohani and Turki Turki
Melanoma skin cancer is one of the most dangerous types of skin cancer, which, if not diagnosed early, may lead to death. Therefore, an accurate diagnosis is needed to detect melanoma. Traditionally, a dermatologist utilizes a microscope to inspect and t...
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Felix D. Beacher, Lilianne R. Mujica-Parodi, Shreyash Gupta and Leonardo A. Ancora
The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual ...
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Roberta Fusco, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria Luisa Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti and Antonella Petrillo
Purpose: The aim of the study was to estimate the diagnostic accuracy of textural, morphological and dynamic features, extracted by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, by carrying out univariate and multivariate statist...
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