446 research outputs found
Effective Features to Classify Skin Lesions in Dermoscopic images
Features such as shape and color are indispensable to determine whether a skin lesion is a melanoma
or not. However, there are no fixed guidelines to define which features are effective and how to combine them for classification. This lack of definition impedes the development of the automatic analyses of
dermoscopic images. In this work, a search for effective features was carried out using a support vector
machine. Three image databases were used to verify the feasibility and sensitivity of the automatic classification used. The results showed which features had a major influence on the classification performance,
and confirmed the need to use various types of features in this process
Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review
Skin lesions known as naevi exhibit diverse characteristics such as size,
shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into
play when monitoring for melanoma, referring to a lesion with distinctive
features that sets it apart from other lesions in the vicinity. As lesions
within the same individual typically share similarities and follow a
predictable pattern, an ugly duckling naevus stands out as unusual and may
indicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD)
has become a significant player in the research and development field, as it
combines machine learning techniques with a variety of patient analysis
methods. Its aim is to increase accuracy and simplify decision-making, all
while responding to the shortage of specialized professionals. These automated
systems are especially important in skin cancer diagnosis where specialist
availability is limited. As a result, their use could lead to life-saving
benefits and cost reductions within healthcare. Given the drastic change in
survival when comparing early stage to late-stage melanoma, early detection is
vital for effective treatment and patient outcomes. Machine learning (ML) and
deep learning (DL) techniques have gained popularity in skin cancer
classification, effectively addressing challenges, and providing results
equivalent to that of specialists. This article extensively covers modern
Machine Learning and Deep Learning algorithms for detecting melanoma and
suspicious naevi. It begins with general information on skin cancer and
different types of naevi, then introduces AI, ML, DL, and CAD. The article then
discusses the successful applications of various ML techniques like
convolutional neural networks (CNN) for melanoma detection compared to
dermatologists' performance. Lastly, it examines ML methods for UD naevus
detection and identifying suspicious naevi
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Deep learning based medical image classifiers have shown remarkable prowess
in various application areas like ophthalmology, dermatology, pathology, and
radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD)
systems in real clinical setups is severely limited primarily because their
decision-making process remains largely obscure. This work aims at elucidating
a deep learning based medical image classifier by verifying that the model
learns and utilizes similar disease-related concepts as described and employed
by dermatologists. We used a well-trained and high performing neural network
developed by REasoning for COmplex Data (RECOD) Lab for classification of three
skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and
performed a detailed analysis on its latent space. Two well established and
publicly available skin disease datasets, PH2 and derm7pt, are used for
experimentation. Human understandable concepts are mapped to RECOD image
classification model with the help of Concept Activation Vectors (CAVs),
introducing a novel training and significance testing paradigm for CAVs. Our
results on an independent evaluation set clearly shows that the classifier
learns and encodes human understandable concepts in its latent representation.
Additionally, TCAV scores (Testing with CAVs) suggest that the neural network
indeed makes use of disease-related concepts in the correct way when making
predictions. We anticipate that this work can not only increase confidence of
medical practitioners on CAD but also serve as a stepping stone for further
development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural
Networks (IJCNN) 202
A Review on Skin Disease Classification and Detection Using Deep Learning Techniques
Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches
- …