DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks

Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs). DUNEScan allows users to upload a skin lesion image, and quickly compares the mean and the variance estimates provided by a number of new and traditional CNN models. Moreover, our web server uses the Grad-CAM and UMAP algorithms to visualize the classification manifold for the user’s input, hence providing crucial information about its closeness to skin lesion images from the popular ISIC database. DUNEScan is freely available at: https://www.dunescan.org.

Aplikasi Mobile Deteksi Dini Kanker Kulit Berdasarkan Image Processing

Currently, between 2 and 3 million non-melanoma skin cancers and 132,000 melanoma skin cancers occur globally each year (WHO, 2017). Skin cancer is one type of cancer that can cause death for many people. Because of this, an application is needed to easily detect skin cancer early that the cancer can be handled with more quickly. Besides, consultations with dermatologists have better prognosis (Avilés-Izquierdo et. al., 2016). Due to that, we built an early skin cancer detection application with dermatologist consultation. Our application helps to diagnose skin cancer before it grows into a life-threatening condition and is crucial to preserving lifestyle, future health, and aesthetics. Besides, thanks to online doctor consultations we have, however, getting diagnosed, prescribed and treated for your issues without spending time travelling to and from the doctors and waiting in queues can be just as effective. We used three management techniques such as machine learning to create data pipelines, build a model, and convert the model to TensorFlow lite with post-training quantization. Android to deploy the TensorFlow lite model and create the application. The application has a real-time connection using firebase. Moreover, cloud to create a simple database for doctor and diagnosis services on firebase.

Bio-Inspired Computing-A Dive into Critical Problems, Potential Architecture and Techniques

The integration and development of electronics in the recent years have impacted a major development on the world and humans, one among that is nanotechnology. Nanotechnology has achieved a greater progress in biomedical engineering in diagnosis and treatment, leading to the introduction of nanomaterials for drug delivery, prostheses and implanting. This work describes the Bio-Nano-tools that are developed based on iron oxide properties, automated tools used in the tumor detection, satin bowerbird optimization (SBO) technique employed in diagnosis of skin cancer. This work also highlights the post introduction development of nanomaterials like combination of nanotechnology with Artificial Intelligence (AI) and its impact, advancement of nanomaterials based on their operations, shapes and characteristics that leading to the growth of nanostructures with operations control properties. The paper also highlights the improvement of silicon neuromorphic photonic processors and parallel simulators in the development of bio inspired computing. We are hopeful that this review article provides future directions in Bio-Inspired Computing. HIGHLIGHTS In processing of medical images, noise plays a challenging role. So, reduction of noise is important, with the data that is analyzed in our review, it is shown that noise reduction can be achieved using Gradient and Feature Adaptive Contour (GFAC) model, with effective results There are many algorithms that are used for skin cancer detection, as highlighted in our review. Amongst all the methods, the particle swarm optimization (PSO) algorithm shows impressive results when compared to other models in terms of feature extraction in dermoscopy images Satin bowerbird optimization (SBO) algorithm helps in improving the CNN efficiency. The optimal justification of the hyper parameter numbers in convolutional neural network (CNN) for skin cancer diagnosis can be achieved using an SBO algorithm

Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization

Melanoma is defined as a disease that has been incurable in advanced stages, which shows the vital importance of timely diagnosis and treatment. To diagnose this type of cancer early, various methods and equipment have been used, almost all of which required a visit to the doctor and were not available to the public. In this study, an automated and accurate process to differentiate between benign skin pigmented lesions and malignant melanoma is presented, so that it can be used by the general public, and it does not require special equipment and special conditions in imaging. In this study, after preprocessing of the input images, the region of interest is segmented based on the Otsu method. Then, a new feature extraction is implemented on the segmented image to mine the beneficial characteristics. The process is then finalized by using an optimized Deep Believe Network (DBN) for categorization into 2 classes of normal and melanoma cases. The optimization process in DBN has been performed by a developed version of the newly introduced Thermal Exchange Optimization (dTEO) algorithm to obtain higher efficacy in different terms. To show the method’s superiority, its performance is compared with 7 different techniques from the literature.