Butterfly identification using gray level co-occurrence matrix (glcm) extraction feature and k-nearest neighbor (knn) classification

Gita Persada Butterfly Park is the only breeding of engineered in situ butterflies in Indonesia. It is located in Lampung and has approximately 211 species of breeding butterflies. Each species of Butterflies has a different texture on its wings. The Limited ability of the human eye to distinguishing typical textures on butterfly species is the reason for conducting a research on butterfly identification based on pattern recognition. The dataset consists of 600 images of butterfly’s upper wing from six species: Centhosia penthesilea, Papilio memnon, Papilio nephelus, Pachliopta aristolochiae, Papilio peranthus and Troides helena. The pre-processing stage is conducted using scaling, segmentation and grayscale methods. The GLCM method is used to recognize the characteristics of butterfly images using pixel distance  and Angular direction 0o, 45o, 90o and 135o. The features used is angular second moment, contrast, homogeneity and correlation. KNN classification method in this study uses k values1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 and 23 based on the Rule of Thumb. The result of this study indicate that Centhosia penthesilea and Papilio nephelus classes can be classified properly compared to the other 4 classes and require a classification time of 2 seconds at each angular orientation. The highest accuracy is 91.1% with a value of  in the angle of 90o and error rate8.9%. Classification error occured because the value of the test data features is more dominant with the value of the training image features in different classes than the supposed class.  Another reason is because of imperfect test data

Diagnosis of skin cancer using novel computer vision and deep learning techniques

Recent years have noticed an increase in the total number of skin cancer cases and it is projected to grow exponentially, however mortality rate of malignant melanoma can be decreased if it is diagnosed and treated in its early stage. Notwithstanding the fact that visual similarity between benign and malignant lesions makes the task of diagnosis difficult even for an expert dermatologist, thereby increasing the chances of false prediction. This dissertation proposes two novel methods of computer-aided diagnosis for the classification of malignant lesion. The first method pre-processes the acquired image by the Dull razor method (for digital hair removal) and histogram equalisation. Henceforth the image is segmented by the proposed method using LR-fuzzy logic and it achieves an accuracy, sensitivity and specificity of 96.50%, 97.50% and 96.25% for the PH2 dataset; 96.16%, 91.88% and 98.26% for the ISIC 2017 dataset; 95.91%, 91.62% and 97.37% for ISIC 2018 dataset respectively. Furthermore, the image is classified by the modified You Only Look Once (YOLO v3) classifier and it yields an accuracy, sensitivity and specificity of 98.16%, 95.43%, and 99.50% respectively. The second method enhances the images by removing digital artefacts and histogram equalisation. Thereafter, triangular neutrosophic number (TNN) is used for segmentation of lesion, which achieves an accuracy, sensitivity, and specificity of 99.00%, 97.50%, 99.38% for PH2; 98.83%, 98.48%, 99.01% for ISIC 2017; 98.56%, 98.50%, 98.58% for ISIC 2018; and 97.86%, 97.56%, 97.97% for ISIC 2019 dataset respectively. Furthermore, data augmentation is performed by the addition of artefacts and noise to the training dataset and rotating the images at an angle of 650, 1350, and 2150 such that the training dataset is increased to 92838 from 30946 images. Additionally, a novel classifier based on inception and residual module is trained over augmented dataset and it is able to achieve an accuracy, sensitivity and specificity of 99.50%, 100%, 99.38% for PH2; 99.33%, 98.48%, 99.75% for ISIC 2017; 98.56%, 97.61%, 98.88% for ISIC 2018 and 98.04%, 96.67%, 98.52% for ISIC 2019 dataset respectively. Later in our dissertation, the proposed methods are deployed into real-time mobile applications, therefore enabling the users to diagnose the suspected lesion with ease and accuracy