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Skin cancer classification is a complex and time-consuming task. Existing approaches use segmentation to improve accuracy and efficiency, but due to different sizes and shapes of lesions, segmentation is not a suitable approach. In this research study, we proposed an improved automated system based on hybrid and optimal feature selections. Firstly,...
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... architecture diagram in Figure 1 presents our methodology, dataset HAM10000 is highly imbalanced, so the first step is to balance the dataset using augmentation techniques. After the dataset is balanced, the augmented dataset is passed on to two different CNN models: Darknet53 and Inception V3. ...
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Citations
... Usma et al. [100] suggested a hybrid and optimal feature selection-based improved automated system. The whole experiment is done on HAM10000. ...
The world population is growing very fast and the lifestyle of human beings is changing with time and place. So, there is a need for disease management which includes disease diagnosis, its detection and classification, cure and lastly for future disease prevention. The outermost protective layer of a human body is the skin. Skin not only impacts a person’s health but also psychologically impacts one’s life. Computer-aided systems are very helpful in skin disease detection and classification and their application is growing rapidly in healthcare. This literature review paper aims to help the researchers to get a synthesized and appropriate information for the same. We have included papers from 2021 to 2023 for the review from the Scopus database. 45 studies are selected for the review of which 32 studies use deep learning techniques, 11 use machine learning techniques and 2 studies use a hybrid approach. The studies are compared on various parameters like models, datasets, and performance metrics. The work also identified some of the challenges like dealing with noise and also explained disease symptoms.
... A. Phát hiện ung thư da bằng học sâu và đặc trưng sâu Các phương pháp học sâu đã chứng minh được tính hiệu quả VGG16, VGG19 [9], Xception [10], Resnet [11], Inceptionv3 [12], MobileNet [13], MobileNet-V2 [14], Alexnet [15], DenseNet-201 [16], Inception-ResNet-V2 [17] nên đã được ứng dụng rất nhiều trong các ứng dụng xác định và nhận dạng đối tượng. Nhiều nghiên cứu sử dụng một trong các phương pháp này trong trích rút các đặc trưng sâu để phát hiện ung thư da [18], [19], [20], [21], [22]. ...
... Muhammad và cộng sự [21] đề xuất một hệ thống tự động cải tiến dựa trên việc lựa chọn đặc trưng kết hợp và tối ưu. Nghiên cứu sử dụng mô hình CNN, Darknet53 và Inception V3 bằng cách sử dụng học chuyển giao. ...
Nghiên cứu được thực hiện trên
ba loại ung thư da phổ biến tại Việt Nam, bao gồm:
Ung thư da tế bào basal (Basal Cell Carcinoma -
BCC), ung thư da tế bào biểu mô (Squamous Cell
Carcinoma - SCC) và ung thư da sắc tố (Melanoma
- MM). Bộ cơ sở dữ liệu ung thư da được thu thập
tại Bệnh viện da liễu Trung ương bằng kỹ thuật chụp
ảnh thông thường sử dụng điện thoại thông minh.
Nghiên cứu đề xuất sử dụng ba mô hình mạng nơron
tích chập riêng biệt với các kiến trúc khác nhau để
huấn luyện trên đặc trưng sâu của dữ liệu ảnh bệnh,
các mô hình riêng được kết hợp để đưa ra dự đoán
cuối cùng. Kết quả đạt được độ chính xác 89.8%,
80%, 69% cho ba loại ung thư tương ứng trên
... The traditional methods of diagnosis are costly and time-consuming due to the involvement of trained experts, as well as the requirement of a well-equipped environment. Recent advances in computerized solutions for diagnosis are quite promising, showing increased accuracy and efficiency [3]. By applying medical image-processing techniques to chest X-ray and melanoma skin cancer dermoscopy images, we can assist in detecting diseases earlier and more accurately, which can save many humans. ...
Today, medical image-based diagnosis has advanced significantly in the world. The number of studies being conducted in this field is enormous, and they are producing findings with a significant impact on humanity. The number of databases created in this field is skyrocketing. Examining these data is crucial to find important underlying patterns. Classification is an effective method for identifying these patterns. This work proposes a deep investigation and analysis to evaluate and diagnose medical image data using various classification methods and to critically evaluate these methods’ effectiveness. The classification methods utilized include machine-learning (ML) algorithms like artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes (NB), logistic regression (LR), random subspace (RS), fuzzy logic and a convolution neural network (CNN) model of deep learning (DL). We applied these methods to two types of datasets: chest X-ray datasets to classify lung images into normal and abnormal, and melanoma skin cancer dermoscopy datasets to classify skin lesions into benign and malignant. This work aims to present a model that aids in investigating and assessing the effectiveness of ML approaches and DL using CNN in classifying the medical databases and comparing these methods to identify the most robust ones that produce the best performance in diagnosis. Our results have shown that the used classification algorithms have good results in terms of performance measures.
... Figure 5 shows dermoscopic images, while Figure 6 displays clinical images from the skin cancer dataset. Branching out of skin cancer are skin tumors, which are chiefly responsible for the mortality rate once diagnosed with the same [17]. Skin tumors can be categorized into two types, namely melanoma and non-melanoma. ...
... Tumors are further subdivided into two major categories, as described in the following section. Branching out of skin cancer are skin tumors, which are chiefly responsible for the mortality rate once diagnosed with the same [17]. Skin tumors can be categorized into two types, namely melanoma and non-melanoma. ...
Simple Summary
The proposed research aims to provide a deep insight into the deep learning and machine learning techniques used for diagnosing skin cancer. While maintaining a healthy balance between both Machine Learning as well as Deep Learning, the study also discusses open challenges and future directions in this field. The research includes a comparison on widely used datasets and prevalent review papers discussing skin cancer diagnosis using Artificial Intelligence. The authors of this study aim to set this review as a benchmark for further studies in the field of skin cancer diagnosis by also including limitations and benefits of historical approaches.
Abstract
Skin cancer continues to remain one of the major healthcare issues across the globe. If diagnosed early, skin cancer can be treated successfully. While early diagnosis is paramount for an effective cure for cancer, the current process requires the involvement of skin cancer specialists, which makes it an expensive procedure and not easily available and affordable in developing countries. This dearth of skin cancer specialists has given rise to the need to develop automated diagnosis systems. In this context, Artificial Intelligence (AI)-based methods have been proposed. These systems can assist in the early detection of skin cancer and can consequently lower its morbidity, and, in turn, alleviate the mortality rate associated with it. Machine learning and deep learning are branches of AI that deal with statistical modeling and inference, which progressively learn from data fed into them to predict desired objectives and characteristics. This survey focuses on Machine Learning and Deep Learning techniques deployed in the field of skin cancer diagnosis, while maintaining a balance between both techniques. A comparison is made to widely used datasets and prevalent review papers, discussing automated skin cancer diagnosis. The study also discusses the insights and lessons yielded by the prior works. The survey culminates with future direction and scope, which will subsequently help in addressing the challenges faced within automated skin cancer diagnosis.
Vitiligo lesion segmentation is crucial for the assessment and treatment of vitiligo. There are two significant challenges in this problem, namely, the availability of dense segmentation annotations and the collection of large amounts of vitiligo images, which are also major challenges in medical image analysis (MIA). However, most existing methods often heavily rely on the availability of large-scale labeled datasets and high-quality annotations. Consequently, the performance of these models may not be easily reproducible or transferable to those domains with limited data availability. As a result, there is a need to develop alternative approaches that can leverage unlabeled datasets for segmentation with a small-scale training set. In this paper, we propose a data augmentation strategy based on image editing, which can synthesize a large number of samples using a small number of annotated data. The synthesized examples are of high visual quality and enforce the segmentation performance without any cost. Besides, we also adapt the Mean-Teacher framework for reliable predictions mining from unlabeled samples to alleviate the demands of densely annotated segmentations. We obtain pseudo-labels for unlabeled samples by utilizing highly confident pixels. On the other hand, we proposed a new Bimodal Vitiligo Lesions Segmentation (BVLS) dataset containing fine-grain segmentation masks and bimodal images usually used for vitiligo diagnosis to mitigate the lack of a vitiligo segmentation dataset. Extensive experiments conducted on the BLVS dataset demonstrate that our approach can achieve significant improvements (+17.27%) compared with previous data augmentation methods on the UNet backbone. Furthermore, the semi-supervised framework can reach an IoU of 49.71% with only 10% annotated images. Our code and dataset are availabel at https://github.com/JcWang20/BLVS.
