Identification, Surveillance and Profiling: On the Use and Abuse of Citizen Data

Abstract Background: Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective early detection with key screening approaches such as dermoscopy examinations, leading to stronger recovery prognoses. Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce Cancer-Net SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public. To the best of the authors' knowledge, Cancer-Net SCa comprises the first machine-driven design of deep neural network architectures tailored specifically for skin cancer detection, one of which leverages attention condensers for an efficient self-attention design.Results: We investigate and audit the behaviour of Cancer-Net SCa in a responsible and transparent manner through explainability-driven performance validation. All the proposed designs achieved improved accuracy when compared to the ResNet-50 architecture while also achieving significantly reduced architectural and computational complexity. In addition, when evaluating the decision making process of the networks, it can be seen that diagnostically relevant critical factors are leveraged rather than irrelevant visual indicators and imaging artifacts. Conclusion: The proposed Cancer-Net SCa designs achieve strong skin cancer detection performance on the International Skin Imaging Collaboration (ISIC) dataset, while providing a strong balance between computation and architectural efficiency and accuracy. While Cancer-Net SCa is not a production-ready screening solution, the hope is that the release of Cancer-Net SCa in open source, open access form will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.

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DESIGNING THE INFORMATION SYSTEM FOR DATA COLLECTION OF COVID-19 SYMPTOMS FOR INDONESIAN CITIZENS

Jurnal Teknologi Informasi dan Pendidikan ◽

10.24036/tip.v14i1.386 ◽

2021 ◽

Vol 14 (1) ◽

pp. 26-32

Author(s):

Thamrin Thamrin ◽

Delsina Faiza ◽

Ahmaddul Hadi ◽

Khairi Budayawan ◽

Geovanne Farell ◽

...

Keyword(s):

Information System ◽

Data Collection ◽

Small Computer ◽

The Internet ◽

Literature Study ◽

Human Needs ◽

Computer Device ◽

Technological Developments ◽

System Information ◽

Citizen Data

Information systems are the result of rapidly emerging technological developments because human needs as information users of technology are very strong and rapid. Begin with the very rapid growth of computer technology, computers such as smartphones, tablets, notebooks and others are limited in scale. This small computer device must be connected to the Internet so that it can be used properly. And the information system plays its role as a provider of information that computer users want to access when the computer device is linked to the Internet. The information system developed in this paper is the Citizen Data Collection Information System, this system was created using the YII Framework. The YII framework uses the MVC method which makes it easier to develop a Citizen Data Collection Information System. With this Information System, information about the latest condition of the residents can be easily accessed. So that the Kelurahan, Kecamatan, RW, and RT will have the latest information about their residents and easily anticipate if needed. In the development of this system, it has staged such as literature study, observation, interviews, analysis, design, testing, and implementation. The conclusion is that this information system can carry out the desired function when it is implemented.

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