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

Automated classification of African embroidery patterns using cellular learning automata and support vector machines

Embroidery is the art that is majorly practised in Nigeria, which requires creativity and skills. However, differentiating between two standard embroidery patterns pose challenges to wearers of the patterns. This study developed a classification system to improve the embroiderer to user relationship. The specific characteristics are used as feature sets to classify two common African embroidery patterns (handmade and tinko) are shape, brightness, thickness and colour. The system developed and simulated in MATLAB 2016a environment employed Cellular Learning Automata (CLA) and Support Vector Machine (SVM) as its classifier. The classification performance of the proposed system was evaluated using precision, recall, and accuracy. The system obtained an average precision of 0.93, average recall of 0.81, and average accuracy of 0.97 in classifying the handmade and tinko embroidery patterns considered in this study. This study also presented an experimental result of three validation models for training and testing the dataset used in this study. The model developed an improved and refined embroiderer for eliminating stress related to the manual pattern identification process

Step-wise Integration of Deep Class-specific Learning for Dermoscopic Image Segmentation

The segmentation of abnormal regions on dermoscopic images is an important step for automated computer aided diagnosis (CAD) of skin lesions. Recent methods based on fully convolutional networks (FCN) have been very successful for dermoscopic image segmentation. However, they tend to overfit to the visual characteristics that are present in the dominant non-melanoma studies and therefore, perform poorly on the complex visual characteristics exhibited by melanoma studies, which usually consists of fuzzy boundaries and heterogeneous textures. In this paper, we propose a new method for automated skin lesion segmentation that overcomes these limitations via a novel deep class-specific learning approach which learns the important visual characteristics of the skin lesions of each individual class (melanoma vs non-melanoma) on an individual basis. We also introduce a new probability-based, step-wise integration to combine complementary segmentation results derived from individual class-specific learning models. We achieved an average Dice coefficient of 85.66% on the ISBI 2017 Skin Lesion Challenge (SLC), 91.77% on the ISBI 2016 SLC and 92.10% on the PH2 datasets with corresponding Jaccard indices of 77.73%, 85.92% and 85.90%, respectively, for the same datasets. Our experiments on three well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation

COMPARATIVE STUDY FOR MELANOMA SEGMENTATION IN SKIN LESION IMAGES

Melanoma is the leading cause of fatalities among skin can-cers and the discovery of the pathology in the early stagesis essential to increase the chances of cure. Computationalmethods through medical imaging are being developed tofacilitate the detection of melanoma. To interpret informa-tion in these images eciently, it is necessary to isolate theaected region. In our research, a comparison was made be-tween segmentation techniques, rstly a method based onthe Otsu algorithm, secondly the K-means clustering algo-rithm and nally,the U-net deep learning was developed.The tests performed on the PH2 images base had promisingresults, especially U-net

Adversarial Turing Patterns from Cellular Automata

State-of-the-art deep classifiers are intriguingly vulnerable to universal adversarial perturbations: single disturbances of small magnitude that lead to misclassification of most in-puts. This phenomena may potentially result in a serious security problem. Despite the extensive research in this area,there is a lack of theoretical understanding of the structure of these perturbations. In image domain, there is a certain visual similarity between patterns, that represent these perturbations, and classical Turing patterns, which appear as a solution of non-linear partial differential equations and are underlying concept of many processes in nature. In this paper,we provide a theoretical bridge between these two different theories, by mapping a simplified algorithm for crafting universal perturbations to (inhomogeneous) cellular automata,the latter is known to generate Turing patterns. Furthermore,we propose to use Turing patterns, generated by cellular automata, as universal perturbations, and experimentally show that they significantly degrade the performance of deep learning models. We found this method to be a fast and efficient way to create a data-agnostic quasi-imperceptible perturbation in the black-box scenario. The source code is available at https://github.com/NurislamT/advTuring.Comment: Published as a conference paper at AAAI 2021 (camera-ready version

Transition region based approach for skin lesion segmentation

Skin melanoma is a skin disease that affects nearly 40% of people globally. Manual detection of the area is a time-consuming process and requires expert knowledge. The application of computer vision techniques can simplify this. In this article, a novel unsupervised transition region based approach for skin lesion segmentation for melanoma detection is proposed. The method starts with Gaussian blurring of the green channel dermoscopic image. Further, the transition region is extracted using local variance features and a global thresholding operation. It achieves the region of interest (binary mask) using various morphological operations. Finally, the melanoma regions are segregated from normal skin regions using the binary mask. The proposed method is tested using DermQuest dataset along with ISIC 2017 dataset and it achieves better results as compared to other state of art methods in effectively segmenting the melanoma regions from the normal skin regions