Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

Kalwa, Upender; Legner, Christopher; Kong, Taejoon; Pandey, Santosh

doi:10.3390/sym11060790

Cited by 34 publications

(27 citation statements)

References 55 publications

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“…Kalwa et al [17] a Smartphone Application was used to diagnose melanoma which is a type of skin cancer, considered the most deadly and difficult to treat in advanced stages. The application analyzes images and compares them with 200 images of a public dataset.…”

Section: Related Workmentioning

confidence: 99%

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

et al. 2020

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Guillain–Barré Syndrome (GBS) is an unusual disorder where the body’s immune system affects the peripheral nervous system. GBS has four main subtypes, whose treatments vary among them. Severe cases of GBS can be fatal. This work aimed to investigate whether balancing an original GBS dataset improves the predictive models created in a previous study. purpleBalancing a dataset is to pursue symmetry in the number of instances of each of the classes.The dataset includes 129 records of Mexican patients diagnosed with some subtype of GBS. We created 10 binary datasets from the original dataset. Then, we balanced these datasets using four different methods to undersample the majority class and one method to oversample the minority class. Finally, we used three classifiers with different approaches to creating predictive models. The results show that balancing the original dataset improves the previous predictive models. The goal of the predictive models is to identify the GBS subtypes applying Machine Learning algorithms. It is expected that specialists may use the model to have a complementary diagnostic using a reduced set of relevant features. Early identification of the subtype will allow starting with the appropriate treatment for patient recovery. This is a contribution to exploring the performance of balancing techniques with real data.

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Section: Related Workmentioning

confidence: 99%

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

et al. 2020

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“…Table of biosensors for personal mobile health measurement devices summarizing function, key materials and properties, and technical specifications, both on-and off-phone. Type of sensor Example of measured health parameter Technical capability SI units Class Common materials Key property Typical specifications On-phone Off-phone Camera Optical assay readout [38] or microscopy [42] Record image -CMOS Silicon Semiconductor/ photosensitive Resolution (MP) 16 [135] 24.5 [136] Silicon dioxide Insulator…”

Section: Wwwadvmattechnoldementioning

confidence: 99%

“…Kalwa et al demonstrated a fully integrated smartphone application for the detection of melanoma at the POC. [38] The user takes an image of the target skin site using the smartphone with a 10x detachable lens, which is then subjected to a number of image processing steps such as preprocessing, segmentation, and feature extraction. A support vector machine classifier is then used to determine whether the image is indicative of a malignant or benign lesion.…”

Section: Adapter-dependent Applicationsmentioning

confidence: 99%

Biosensors for Personal Mobile Health: A System Architecture Perspective

Arumugam

Colburn

Sia

2019

Adv Materials Technologies

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Advances in mobile biosensors, integrating developments in materials science and instrumentation, are fueling an expansion in health data being collected and analyzed in decentralized settings. For example, semiconductor‐based sensors are enabling measurement of vital signs, and microfluidic‐based sensors are enabling measurement of biochemical markers. As biosensors for mobile health are becoming increasingly paired with smart devices, it can become critical for researchers to design biosensors—with appropriate functionalities and specifications—to work seamlessly with accompanying connected hardware and software. Here, recent research in biosensors, as well as current mobile health devices in use is described and classified into four distinct system architectures that take into account the biosensing and data processing functions required in personal mobile health devices. The path forward for integrating biosensors into smartphone‐based mobile health devices is also discussed.

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“…Skin cancer diagnostics with an all-inclusive smartphone application [16] 2 Detection of melanoma from dermoscopic images of naevi acquired under uncontrolled conditions [17] 3 Automatic color segmentation of images with applications in detection of variegated coloring in skin tumors [18] 4 Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule [19] 5 Automated malignant melanoma detection using Matlab [20] https://doi.org/10.1371/journal.pone.0234352.t002…”

mentioning

confidence: 99%

“…Simple matlab tool for automated malignant melanoma diagnosis [22] 2 Skin cancer diagnostics with an all-inclusive smartphone application [16] 3…”

mentioning

confidence: 99%

Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images

Ali

O’Shea

2020

PLoS ONE

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Asymmetry, color variegation and diameter are considered strong indicators of malignant melanoma. The subjectivity inherent in the first two features and the fact that 10% of melanomas tend to be missed in the early diagnosis due to having a diameter less than 6mm, deem it necessary to develop an objective computer vision system to evaluate these criteria and aid in the early detection of melanoma which could eventually lead to a higher 5-year survival rate. This paper proposes an approach for evaluating the three criteria objectively, whereby we develop a measure to find asymmetry with the aid of a decision tree which we train on the extracted asymmetry measures and then use to predict the asymmetry of new skin lesion images. A range of colors that demonstrate the suspicious colors for the color variegation feature have been derived, and Feret's diameter has been utilized to find the diameter of the skin lesion. The decision tree is 80% accurate in determining the asymmetry of skin lesions, and the number of suspicious colors and diameter values are objectively identified.

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Skin Cancer Diagnostics with an All-Inclusive Smartphone Application

Cited by 34 publications

References 55 publications

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

Classification of Guillain–Barré Syndrome Subtypes Using Sampling Techniques with Binary Approach

Biosensors for Personal Mobile Health: A System Architecture Perspective

Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images

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