scholarly journals Automated Non-invasive Diagnosis of Melanoma Skin Cancer using Dermo-scopic Images

2020 ◽  
Vol 32 ◽  
pp. 03029
Author(s):  
Huda Khan ◽  
Anushka Yadav ◽  
Reha Santiago ◽  
Sangita Chaudhari
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Skin Biopsy ◽  
The Body ◽  
Invasive Technique ◽  
Non Invasive ◽  
Malignant Skin ◽  
Keywords Melanoma ◽  
Biopsy Method ◽  
Melanoma Skin

Melanoma skin cancer is one of the deadliest cancers today, the rate of which is rising exponentially. If not detected and treated early, it will most likely spread to other parts of the body. To properly detect melanoma, a skin biopsy is required. This is an invasive technique which is why the need for a diagnosis system that can eradicate the skin biopsy method arises. It is observed that the proposed method is successfully detecting and correctly classifying the malignant and non-malignant skin cancer. Finally, a neural network is used to classify benign and malignant images from the extracted features. Keywords: Melanoma, non-invasive, skin lesion, neural network.

scholarly journals Epidemiology of basal cell carcinomas and squamous cell carcinomas in a Department of Dermatology: a 5 year review

2012 ◽  
Vol 87 (2) ◽  
pp. 212-219 ◽  
Author(s):  
Pedro Andrade ◽  
Maria Manuel Brites ◽  
Ricardo Vieira ◽  
Angelina Mariano ◽  
José Pedro Reis ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Squamous Cell ◽  
Basal Cell ◽  
Skin Neoplasms ◽  
Squamous Cell Carcinomas ◽  
Exposed Skin ◽  
Non Melanoma Skin Cancer ◽  
Basal Cell Carcinomas ◽  
Malignant Skin ◽  
Melanoma Skin

BACKGROUND: Non-melanoma skin cancer, a common designation for both basal cell carcinomas and squamous cell carcinomas, is the most frequent malignant skin neoplasm. OBJECTIVE: Epidemiologic characterization of the population with Non-melanoma skin cancer. METHODS: Retrospective analysis of all patients diagnosed with Non-melanoma skin cancer based on histopathologic analysis of all incisional or excisional skin biopsies performed between 2004 and 2008 in a Department of Dermatology. RESULTS: A total of 3075 Non-melanoma skin cancers were identified, representing 88% of all malignant skin neoplasms (n=3493) diagnosed in the same period. Of those, 68,3% were basal cell carcinomas. Most Non-melanoma skin cancer patients were female and over 60 years old. Of all Non-melanoma skin cancer, 81,7% (n=1443) were located in sun-exposed skin, and represented 95,1% of malignant skin neoplasms in sun-exposed skin. Non-melanoma skin cancer was the most frequent malignant skin neoplasm in most topographic locations, except for abdomen and pelvis - over 95% of all malignant skin neoplasms in the face, neck and scalp were Non-melanoma skin cancer. Basal cell carcinomas were clearly predominant in all locations, except in upper and lower limbs, lower lip and genitals, where squamous cell carcinomas represented respectively 77,7%, 77,4%, 94,7% and 95,3% of the Non-melanoma skin cancers. CONCLUSION: Being the most common skin cancer, Non-melanoma skin cancer should be under constant surveillance, in order to monitor its epidemiologic dynamics, the efficiency of preventive measures and the adaptation of the healthcare resources.

scholarly journals Rapid identification of wood species using XRF and neural network machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aaron N. Shugar ◽  
B. Lee Drake ◽  
Greg Kelley
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Wood Species ◽  
Cost Effective ◽  
Rapid Identification ◽  
Invasive Technique ◽  
X Ray ◽  
Non Invasive ◽  
Cost Effective Alternative

AbstractAn innovative approach for the rapid identification of wood species is presented. By combining X-ray fluorescence spectrometry with convolutional neural network machine learning, 48 different wood specimens were clearly differentiated and identified with a 99% accuracy. Wood species identification is imperative to assess illegally logged and transported lumber. Alternative options for identification can be time consuming and require some level of sampling. This non-invasive technique offers a viable, cost-effective alternative to rapidly and accurately identify timber in efforts to support environmental protection laws and regulations.

scholarly journals Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
David Roffman ◽  
Gregory Hart ◽  
Michael Girardi ◽  
Christine J. Ko ◽  
Jun Deng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Skin Cancer ◽  
Non Melanoma Skin Cancer ◽  
Artificial Neural ◽  
Melanoma Skin

scholarly journals Detection of Skin Cancer using Deep CNN

2020 ◽  
Vol 8 (5S) ◽  
pp. 22-24
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
The Body ◽  
The Sun ◽  
Layer System ◽  
Data Set ◽  
Abnormal Cells ◽  
Deep Cnn ◽  
Uv Rays

Development of abnormal cells are the cause of skin cancer that have the ability to attack or spread to various parts of the body. The skin cancer signs may include mole that has varied in size, shape, color, and may also haveno –uniform edges, might be having multiple colours, and would itch orevn bleed in some cases. The exposure to the UV-rays from the sun is considered to be accountable for more than 90% of the Skin Cancer cases which are recorded.In this paper, the development of a classificiation system for skin cancer, is discussed, using Convolutional Neural Network which would help in classifying the cancer usingTensorFlow and Keras as Malignantor Benign. The collected images from the data set are fed into the system and it is processed to classify the skin cancer. After the implementation the accuracy of the Convolutional 2-D layer system developed is found to be 78%.

Development of a non-invasive blood sugar sensor

Impact ◽  
2019 ◽  
Vol 2019 (8) ◽  
pp. 38-40
Author(s):  
Koichi Yamakawa
Keyword(s):  
Blood Sugar ◽  
Blood Sugar Level ◽  
Research Team ◽  
The Body ◽  
Invasive Technique ◽  
Risk Of Infection ◽  
Non Invasive ◽  
Finger Prick

A person with diabetes mellitus, which is commonly referred to as diabetes and exists in two forms (type 1 and type 2) must inject themselves with insulin to manage their blood sugar level. This is because the disease causes a person's blood sugar level to become too high and insulin, a hormone produced in the pancreas, helps the body to use sugar for energy. In type 1 diabetes the body's immune system attacks and destroys the cells that produce insulin and in type 2 the pancreas is unable to make enough insulin or the insulin it does make doesn't work properly. As such, in both types of the disease, insulin must be injected into the body and injecting becomes an essential part of the daily regimes of people with diabetes. As you can imagine, this, coupled with the need to regularly check blood sugar with finger prick tests, can be inconvenient as well as painful. In addition, there is the risk of infection. However, there is currently no alternative. A Japan-based research team is working on developing a non-invasive technique for measuring blood glucose.

Skin cancer

2007 ◽  
pp. 465-468
Keyword(s):  
Skin Cancer ◽  
Malignant Melanoma ◽  
Uv Radiation ◽  
Basal Layer ◽  
The Body ◽  
Malignant Melanomas ◽  
Non Melanoma Skin Cancer ◽  
Melanoma Skin

Malignant melanoma 466 Non-melanoma skin cancer 468 • Malignant melanomas arise from melanocytes, mainly found in the basal layer of skin. These cells produce melanin and are responsible for the tanning response after exposure to ultraviolet (UV) radiation. • A few melanocytes exist elsewhere in the body—this explains the rare melanomas that can occur elsewhere, e.g. intracocular, oesophageal....

scholarly journals Non-invasive spectroscopic techniques in the diagnosis of non-melanoma skin cancer

2017 ◽  
Vol 931 ◽  
pp. 012036 ◽  
Author(s):  
E Drakaki ◽  
IA Sianoudis ◽  
EN Zois ◽  
M Makropoulou ◽  
AA Serafetinides ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Spectroscopic Techniques ◽  
Non Invasive ◽  
Non Melanoma Skin Cancer ◽  
Melanoma Skin

scholarly journals Using Thermal Imaging to Monitor Body Temperature of Koalas (Phascolarctos cinereus) in A Zoo Setting

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1094 ◽  
Author(s):  
Edward Narayan ◽  
Annabella Perakis ◽  
Will Meikle
Keyword(s):  
Body Temperature ◽  
Thermal Imaging ◽  
Native Species ◽  
The Body ◽  
Invasive Technique ◽  
Phascolarctos Cinereus ◽  
Non Invasive ◽  
Imaging Tool ◽  
Potential Applicability ◽  
Invasive Techniques

Non-invasive techniques can be applied for monitoring the physiology and behaviour of wildlife in Zoos to improve management and welfare. Thermal imaging technology has been used as a non-invasive technique to measure the body temperature of various domesticated and wildlife species. In this study, we evaluated the application of thermal imaging to measure the body temperature of koalas (Phascolarctos cinereus) in a Zoo environment. The aim of the study was to determine the body feature most suitable for recording a koala’s body temperature (using coefficient of variation scores). We used a FLIR530TM IR thermal imaging camera to take images of each individual koala across three days in autumn 2018 at the Wildlife Sydney Zoo, Australia. Our results demonstrated that koalas had more than one reliable body feature for recording body temperature using the thermal imaging tool—the most reliable features were eyes and abdomen. This study provides first reported application of thermal imaging on an Australian native species in a Zoo and demonstrates its potential applicability as a humane/non-invasive technique for assessing the body temperature as an index of stress.

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