A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

The detection and classification of blast cell in Leukaemia Acute Promyelocytic Leukaemia (AML M3) blood using simulated annealing and neural networks

This paper was delivered at AIME 2011: 13th Conference on Artifical Intelligence in Medicine.This paper presents a method for the detection and classification of blast cells in M3 with others sub-types using simulated annealing and neural networks. In this paper, we increased our test result from 10 images to 20 images. We performed Hill Climbing, Simulated Annealing and Genetic Algorithms for detecting the blast cells. As a result, simulated annealing is the “best” heuristic search for detecting the leukaemia cells. From the detection, we performed features extraction on the blast cells and we classifying based on M3 and other sub-types using neural networks. We received convincing result which has targeting around 97% in classifying of M3 with other sub-types. Our results are based on real world image data from a Haematology Department.Universiti Sains Islam Malaysia and the Ministry of Higher Education, Malaysi

Prostate Cancer Nodal Staging: Using Deep Learning to Predict 68Ga-PSMA-Positivity from CT Imaging Alone

Lymphatic spread determines treatment decisions in prostate cancer (PCa) patients. 68Ga-PSMA-PET/CT can be performed, although cost remains high and availability is limited. Therefore, computed tomography (CT) continues to be the most used modality for PCa staging. We assessed if convolutional neural networks (CNNs) can be trained to determine 68Ga-PSMA-PET/CT-lymph node status from CT alone. In 549 patients with 68Ga-PSMA PET/CT imaging, 2616 lymph nodes were segmented. Using PET as a reference standard, three CNNs were trained. Training sets balanced for infiltration status, lymph node location and additionally, masked images, were used for training. CNNs were evaluated using a separate test set and performance was compared to radiologists' assessments and random forest classifiers. Heatmaps maps were used to identify the performance determining image regions. The CNNs performed with an Area-Under-the-Curve of 0.95 (status balanced) and 0.86 (location balanced, masked), compared to an AUC of 0.81 of experienced radiologists. Interestingly, CNNs used anatomical surroundings to increase their performance, "learning" the infiltration probabilities of anatomical locations. In conclusion, CNNs have the potential to build a well performing CT-based biomarker for lymph node metastases in PCa, with different types of class balancing strongly affecting CNN performance

Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.

Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system\u27s use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications

Diagnóstico automático de melanoma mediante técnicas modernas de aprendizaje automático

The incidence and mortality rates of skin cancer remain a huge concern in many countries. According to the latest statistics about melanoma skin cancer, only in the Unites States, 7,650 deaths are expected in 2022, which represents 800 and 470 more deaths than 2020 and 2021, respectively. In 2022, melanoma is ranked as the fifth cause of new cases of cancer, with a total of 99,780 people. This illness is mainly diagnosed with a visual inspection of the skin, then, if doubts remain, a dermoscopic analysis is performed. The development of e_ective non-invasive diagnostic tools for the early stages of the illness should increase quality of life, and decrease the required economic resources. The early diagnosis of skin lesions remains a tough task even for expert dermatologists because of the complexity, variability, dubiousness of the symptoms, and similarities between the different categories among skin lesions. To achieve this goal, previous works have shown that early diagnosis from skin images can benefit greatly from using computational methods. Several studies have applied handcrafted-based methods on high quality dermoscopic and histological images, and on top of that, machine learning techniques, such as the k-nearest neighbors approach, support vector machines and random forest. However, one must bear in mind that although the previous extraction of handcrafted features incorporates an important knowledge base into the analysis, the quality of the extracted descriptors relies heavily on the contribution of experts. Lesion segmentation is also performed manually. The above procedures have a common issue: they are time-consuming manual processes prone to errors. Furthermore, an explicit definition of an intuitive and interpretable feature is hardly achievable, since it depends on pixel intensity space and, therefore, they are not invariant regarding the differences in the input images. On the other hand, the use of mobile devices has sharply increased, which offers an almost unlimited source of data. In the past few years, more and more attention has been paid to designing deep learning models for diagnosing melanoma, more specifically Convolutional Neural Networks. This type of model is able to extract and learn high-level features from raw images and/or other data without the intervention of experts. Several studies showed that deep learning models can overcome handcrafted-based methods, and even match the predictive performance of dermatologists. The International Skin Imaging Collaboration encourages the development of methods for digital skin imaging. Every year since 2016 to 2019, a challenge and a conference have been organized, in which more than 185 teams have participated. However, convolutional models present several issues for skin diagnosis. These models can fit on a wide diversity of non-linear data points, being prone to overfitting on datasets with small numbers of training examples per class and, therefore, attaining a poor generalization capacity. On the other hand, this type of model is sensitive to some characteristics in data, such as large inter-class similarities and intra-class variances, variations in viewpoints, changes in lighting conditions, occlusions, and background clutter, which can be mostly found in non-dermoscopic images. These issues represent challenges for the application of automatic diagnosis techniques in the early phases of the illness. As a consequence of the above, the aim of this Ph.D. thesis is to make significant contributions to the automatic diagnosis of melanoma. The proposals aim to avoid overfitting and improve the generalization capacity of deep models, as well as to achieve a more stable learning and better convergence. Bear in mind that research into deep learning commonly requires an overwhelming processing power in order to train complex architectures. For example, when developing NASNet architecture, researchers used 500 x NVidia P100s - each graphic unit cost from $5,899 to$7,374, which represents a total of $2,949,500.00 -$3,687,000.00. Unfortunately, the majority of research groups do not have access to such resources, including ours. In this Ph.D. thesis, the use of several techniques has been explored. First, an extensive experimental study was carried out, which included state-of-the-art models and methods to further increase the performance. Well-known techniques were applied, such as data augmentation and transfer learning. Data augmentation is performed in order to balance out the number of instances per category and act as a regularizer in preventing overfitting in neural networks. On the other hand, transfer learning uses weights of a pre-trained model from another task, as the initial condition for the learning of the target network. Results demonstrate that the automatic diagnosis of melanoma is a complex task. However, different techniques are able to mitigate such issues in some degree. Finally, suggestions are given about how to train convolutional models for melanoma diagnosis and future interesting research lines were presented. Next, the discovery of ensemble-based architectures is tackled by using genetic algorithms. The proposal is able to stabilize the training process. This is made possible by finding sub-optimal combinations of abstract features from the ensemble, which are used to train a convolutional block. Then, several predictive blocks are trained at the same time, and the final diagnosis is achieved by combining all individual predictions. We empirically investigate the benefits of the proposal, which shows better convergence, mitigates the overfitting of the model, and improves the generalization performance. On top of that, the proposed model is available online and can be consulted by experts. The next proposal is focused on designing an advanced architecture capable of fusing classical convolutional blocks and a novel model known as Dynamic Routing Between Capsules. This approach addresses the limitations of convolutional blocks by using a set of neurons instead of an individual neuron in order to represent objects. An implicit description of the objects is learned by each capsule, such as position, size, texture, deformation, and orientation. In addition, a hyper-tuning of the main parameters is carried out in order to ensure e_ective learning under limited training data. An extensive experimental study was conducted where the fusion of both methods outperformed six state-of-the-art models. On the other hand, a robust method for melanoma diagnosis, which is inspired on residual connections and Generative Adversarial Networks, is proposed. The architecture is able to produce plausible photorealistic synthetic 512 x 512 skin images, even with small dermoscopic and non-dermoscopic skin image datasets as problema domains. In this manner, the lack of data, the imbalance problems, and the overfitting issues are tackled. Finally, several convolutional modes are extensively trained and evaluated by using the synthetic images, illustrating its effectiveness in the diagnosis of melanoma. In addition, a framework, which is inspired on Active Learning, is proposed. The batch-based query strategy setting proposed in this work enables a more faster training process by learning about the complexity of the data. Such complexities allow us to adjust the training process after each epoch, which leads the model to achieve better performance in a lower number of iterations compared to random mini-batch sampling. Then, the training method is assessed by analyzing both the informativeness value of each image and the predictive performance of the models. An extensive experimental study is conducted, where models trained with the proposal attain significantly better results than the baseline models. The findings suggest that there is still space for improvement in the diagnosis of skin lesions. Structured laboratory data, unstructured narrative data, and in some cases, audio or observational data, are given by radiologists as key points during the interpretation of the prediction. This is particularly true in the diagnosis of melanoma, where substantial clinical context is often essential. For example, symptoms like itches and several shots of a skin lesion during a period of time proving that the lesion is growing, are very likely to suggest cancer. The use of different types of input data could help to improve the performance of medical predictive models. In this regard, a _rst evolutionary algorithm aimed at exploring multimodal multiclass data has been proposed, which surpassed a single-input model. Furthermore, the predictive features extracted by primary capsules could be used to train other models, such as Support Vector Machine