|
|
|
|
|
Giampaolo D?Alessandro, Pantea Tavakolian and Stefano Sfarra
The present review aims to analyze the application of infrared thermal imaging, aided by bio-heat models, as a tool for the diagnosis of skin and breast cancers. The state of the art of the related technical procedures, bio-heat transfer modeling, and th...
ver más
|
|
|
|
|
|
|
|
|
|
|
Hassan El-khatib, Ana-Maria ?tefan and Dan Popescu
The incidence of melanoma cases continues to rise, underscoring the critical need for early detection and treatment. Recent studies highlight the significance of deep learning in melanoma detection, leading to improved accuracy. The field of computer-ass...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
Muhammad Asad Arshed, Shahzad Mumtaz, Muhammad Ibrahim, Saeed Ahmed, Muhammad Tahir and Muhammad Shafi
Skin cancer, particularly melanoma, has been recognized as one of the most lethal forms of cancer. Detecting and diagnosing skin lesions accurately can be challenging due to the striking similarities between the various types of skin lesions, such as mel...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
Shengnan Hao, Haotian Wu, Yanyan Jiang, Zhanlin Ji, Li Zhao, Linyun Liu and Ivan Ganchev
Accurate segmentation of lesions can provide strong evidence for early skin cancer diagnosis by doctors, enabling timely treatment of patients and effectively reducing cancer mortality rates. In recent years, some deep learning models have utilized compl...
ver más
|
|
|
|
|
|
|
|
|
|
|
Md Rezaul Hoque Khan, Atiqul Alam Chowdhury, Mohammad Rakibul Islam, Md Sanowar Hosen, Mhamud Hasan Mim and Mirza Muntasir Nishat
For the quick identification of diverse types of cancer/malignant cells in the human body, a new hollow-core optical waveguide based on Photonic Crystal Fiber (PCF) is proposed and numerically studied. The refractive index (RI) differs between normal and...
ver más
|
|
|
|
|
|
|
|
|
|
|
Premaladha Jayaraman, Nirmala Veeramani, Raghunathan Krishankumar, Kattur Soundarapandian Ravichandran, Fausto Cavallaro, Pratibha Rani and Abbas Mardani
In recent years, skin cancer diagnosis has been aided by the most sophisticated and advanced machine learning algorithms, primarily implemented in the spatial domain. In this research work, we concentrated on two crucial phases of a computer-aided diagno...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|