Fine Tuning CNN Pre-trained Model Based on Thermal Imaging for Obesity Early Detection

Obesity is a complex disease that causes serious impact health, such as diabetes mellitus, cardiovascular disease, cancer, and stroke. An early obesity diagnosis/ detection method is required to prevent the increasing number of obese people. This study aims to: (i) fine-tune the pre-trained Convolutional Neural Network (CNN) models to build an early detection of obesity and (ii) evaluate the model performance in terms of classifying performance, computation speed, and learning performance. The thermal images acquisition procedure was conducted with 18 normal subjects and 15 obese subjects to build a thermal images dataset of obesity. Pre-trained CNN models: VGG19, MobileNet, ResNet152V, and DenseNet201 were modified and trained using the acquired dataset as the input. The training results show that the DenseNet201 model outperformed other models regarding classifying accuracy: 83.33 % and learning performances. At the same time, the MobileNet model outperformed other models in terms of computation speed with training elapsed time: 12 seconds/epoch. The proposed DenseNet201 model was suitable for implementation as an early screening system of obesity for health workers or physicians. Meanwhile, the proposed MobileNet model was suitable for mobile applications' early detection/diagnosis of obesity

A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

Effect of video learning multimedia toward health belief model of self breast examination

Breast cancer is the most commonly diagnosed cancer and the second leading cause of death from cancer for women. The prevalence of cancer in Indonesia is 1.4 per 1000 inhabitants or about 330,000 people. VLM (Video Learning Multimedia) is a learning tool or medium using video or mobile display, using a combination of text, graphics, sound, video and animation. Aim of study was measure effect of VLM toward self breast examination behavior. This type of research is quantitative research with a population of 135. As for the number of samples in this study as many as 60 respondents, which are divided into two groups namely, intervention group and control group. We used paired sample test with 95% confidence level, obtained p-value= 0.001, meaning there was a difference in attitude before and after being educated on breast check behavior (SADARI) in the control group. Factors that facilitate the change in a person's behavior, consisting of knowledge, attitudes, and cultural values.  It is expected that it will be useful for mothers in Buakana Village rappocini sub-district and society especially women who are more at high risk of breast cancer to not only study but perform breast examination themselves (SADARI) as an early detection of breast cance

Sensor, Signal, and Imaging Informatics in 2017.

Objective To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.Methods PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.Results The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.ConclusionThe growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics

Accuracy of mobile digital teledermoscopy for skin self-examinations in adults at high risk of skin cancer: an open-label, randomised controlled trial

Background: Skin self-examinations supplemented with mobile teledermoscopy might improve early detection of skin cancers compared with naked-eye skin self-examinations. We aimed to assess whether mobile teledermoscopy-enhanced skin self-examination can improve sensitivity and specificity of self-detection of skin cancers when compared with naked-eye skin self-examination. Methods: This randomised, controlled trial was done in Brisbane (QLD, Australia). Eligible participants (aged ≥18 years) had at least two skin cancer risk factors as self-reported in the eligibility survey and had to own or have access to an iPhone compatible with a dermatoscope attachment (iPhone versions 5–8). Participants were randomly assigned (1:1), via a computer-generated randomisation procedure, to the intervention group (mobile dermoscopy-enhanced self-skin examination) or the control group (naked-eye skin self-examination). Control group and intervention group participants received web-based instructions on how to complete a whole body skin self-examination. All participants completed skin examinations at baseline, 1 month, and 2 months; intervention group participants submitted photographs of suspicious lesions to a dermatologist for telediagnosis after each skin examination and control group participants noted lesions on a body chart that was sent to the research team after each skin examination. All participants had an in-person whole-body clinical skin examination within 3 months of their last skin self-examination. Primary outcomes were sensitivity and specificity of skin self-examination, patient selection of clinically atypical lesions suspicious for melanoma or keratinocyte skin cancers (body sites examined, number of lesions photographed, types of lesions, and lesions missed), and diagnostic concordance of telediagnosis versus in-person whole-body clinical skin examination diagnosis. All primary outcomes were analysed in the modified intention-to-treat population, which included all patients who had a clinical skin examination within 3 months of their last skin self-examination. This trial was registered with the Australian and New Zealand Clinical Trials Registry, ACTRN12616000989448. Findings: Between March 6, 2017, and June 7, 2018, 234 participants consented to enrol in the study, of whom 116 (50%) were assigned to the intervention group and 118 (50%) were assigned to the control group. 199 participants (98 participants in the intervention group and 101 participants in the control group) attended the clinical skin examination and thus were eligible for analyses. Participants in the intervention group submitted 615 lesions (median 6·0 per person; range 1–24) for telediagnosis and participants in the control group identified and recorded 673 lesions (median 6·0 per person; range 1–16). At the lesion level, sensitivity for lesions clinically suspicious for skin cancer was 75% (95% CI 63–84) in the intervention group and 88% (95% CI 80–91) in the control group (p=0·04). Specificity was 87% (95% CI 85–90) in the intervention group and 89% (95% CI 87–91) in the control group (p=0·42). At the individual level, the intervention group had a sensitivity of 87% (95% CI 76–99) compared with 97% (95% CI 91–100) in the control group (p=0·26), and a specificity of 95% (95% CI 90–100) compared with 96% (95% CI 91–100) in the control group. The overall diagnostic concordance between the telediagnosis and in-person clinical skin examination was 88%. Interpretation: The use of mobile teledermoscopy did not increase sensitivity for the detection of skin cancers compared with naked-eye skin self-examination; thus, further evidence is necessary for inclusion of skin self-examination technology for public health benefit. Funding: National Health and Medical Research Council (Australia)

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations