Wyniki wyszukiwania - BazTech

1

Skin lesion detection using deep learning

Chandra Rajit, Hajiarbabi Mohammadreza

Journal of Automation Mobile Robotics and Intelligent Systems

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2022

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Vol. 16, No. 3

56--64

EN

Skin lesion can be deadliest if not detected early. Early detection of skin lesion can save many lives. Artificial Intelligence and Machine learning is helping healthcare in many ways and so in the diagnosis of skin lesion. Computer aided diagnosis help clinicians in detecting the cancer. The study was conducted to classify the seven classes of skin lesion using very powerful convolutional neural networks. The two pre trained models i.e DenseNet and Incepton-v3 were employed to train the model and accuracy, precision, recall, f1score and ROCAUC was calculated for every class prediction. Moreover, gradient class activation maps were also used to aid the clinicians in determining what are the regions of image that influence model to make a certain decision. These visualizations are used for explain ability of the model. Experiments showed that DenseNet performed better then Inception V3. Also it was noted that gradient class activation maps highlighted different regions for predicting same class. The main contribution was to introduce medical aided visualizations in lesion classification model that will help clinicians in understanding the decisions of the model. It will enhance the reliability of the model. Also, different optimizers were employed with both models to compare the accuracies.

2

An overview of classification methods from dermoscopy images in skin lesion diagnostic

Michalska Magdalena, Boyko Oksana

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2020

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T. 10, nr 2

36--39

EN

The article contains a review of selected classification methods of dermatoscopic images with human skin lesions, taking into account various stages of dermatological disease. The described algorithms are widely used in the diagnosis of skin lesions, such as artificial neural networks (CNN, DCNN), random forests, SVM, kNN classifier, AdaBoost MC and their modifications. The effectiveness, specificity and accuracy of classifications based on the same data sets were also compared and analyzed.

PL

Artykuł zawiera przegląd wybranych metod klasyfikacji obrazów dermatoskopowych zmian skórnych człowieka z uwzględnieniem różnych etapów choroby dermatologicznej. Opisane algorytmy są szeroko wykorzystywane w diagnostyce zmian skórnych, takie jak sztuczne sieci neuronowe (CNN, DCNN), random forests, SVM, klasyfikator kNN, AdaBoost MC i ich modyfikacje. Porównana i przeanalizowana została również skuteczność, specyficznośc i dokładność klasyfikatów w oparciu o te same zestawy danych.

3

Design of a teledermatology system to support the consultation of dermoscopic cases using mobile technologies and cloud platform

Jaworek-Korjakowska J., Tadeusiewicz R.

Bio-Algorithms and Med-Systems

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2015

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Vol. 11, no. 1

53--58

EN

Skin cancer is the most commonly diagnosed type of cancer in humans regardless of age, gender, or race. One of the most common malignant skin cancers is melanoma, which is a dangerous proliferation of melanocytes. In the last several years, an increasing melanoma incidence has been observed worldwide, and the incidence rate is increasing faster than those of any other skin cancer. The correct identification and diagnosis of moles still creates problems to inexperienced dermatologists and family physicians. In this paper, we present a new approach to the problem of assessing difficult cases in dermatology. We propose a teledermatology system to support the consultation process between family physicians and experts in the field of dermoscopic images. The system consists of a desktop monitoring application and a special smartphone application implemented for experts. If necessary, the physician can send the dermoscopic image to two dermatologists for further examination. This cloud-based architecture provides an interesting system for a fast and efficient exchange of dermatological information. Initial results and assessment of doctors are promising and indicate that the application can be used as a decision support system for dermoscopic images.