International Conference on Artificial Intelligence and Soft Computing

Beczkowski, Michał

Borowski, Norbert

Milczarski, Piotr

Repozytorium Uniwersytetu Łódzkiego (University of Lodz Repository)

Łódź, 22.06.2021Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksMichał Beczkowski, Norbert Borowski, Piotr Milczarski1. Dermoscopy Methodologies2. Dermoscopic Datasets3. Invariant Dataset Augmentation4. PH2 image set preparation5. Pretrained Convolutional Neural Networks features6. Results7. ConclusionsAgendaClassification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksThree-Point Checklist of Dermoscopy (3PLCD)The 3PLCD algorithm relies on the following criteria:• Asymmetry in structure and/or in color in one or two axis of the lesion. The contour shape of the lesion does not impact on the symmetry.• Atypical network, defined as pigmented network with thickened lines and irregular distribution.• Blue-white structures, namely any white and/or blue color visible in the lesion, including blue-white veil, scar-like depigmentation, and regression structures such as peppering.Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksABCD ruleABCD stands for:• asymmetry,• border (not well-defined, irregular),  • color (more than one shade), • diameter (usually larger than 6mm) • evolution (changing features over time). Asymmetry of the lesionis one of the commoncharacteristics of skindamage that can benoticed visuallyDermoscopic Datasets• PH2 – (Mendonca et. al.) 200 images, bmp, 768x568• Derm CS, Derm7pt – (Argenziano et. al.) 1011 cases, jpg, 768x512• ISIC – more than 24000 casesClassification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksPH2 dataset - contains a total of 200 dermoscopicimages of melanocytic lesions, including 80 common nevi, 80 atypical nevi, and 40 melanomas. The PH2 data-base includes medical annotation of all the images namely medical segmentation of the lesion, clinical and histological diagnosis and the assessment of several dermoscopic criteria (colors; pigment network; dots/globules; streaks; regression areas; blue-whitish veil) Invariant Dataset AugmentationThe PH2 database contains 117 fully symmetric, 31 symmetric in one axis and 52 fully asymmetric images of skin lesions. In order to use this database in our research, we had to increase the number of images while minimalizing possible influence on the pixel distribution. To create new images, various geometric transformations that do not change the asymmetry of shape, shade and structure distribution were used :• rotations by 90°, 180° and 270°, • mirroring on the vertical and horizontal axis,• 90° rotation of the images after mirroring.In total, we got seven transformations for each image that did not change the pixels, shape, or color distribution. Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksPH2 image set preparationThe next step in preparing the database was to scale the images to the input sizes required by the selected networks:• scaling the shorter dimension of images (in our case, height) to the input size, e.g.224px using the Bicubic Sharper algorithm in Photoshop • Cropping all images to square shapeThe dataset prepared in this way contains 936 fully symmetric, 248 symmetric in one axis and 416 fully asymmetric images of skin lesions Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksPH2 divisionTo selected networks, two databases were added: training and testing. Both databases were created by dividing the augmentation PH2 dataset into two sets in the following proportions 75% training and validation and 25% testing. The division was carried out so that the original images and their copies were in one set. To check whether increasing the database with image copies obtained after rotations and mirroring gives better results, the tests were carried out on the original PH2 database file.Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksPretrained Convolutional Neural Networks featuresWe used pretrained networks in our research because they are trained on the ImageNet database.Since in each pretrained network the last three layers are configured to classify 1000 classes, we separated all but the last three layers and replaced them so that the networks would classify images into 3 classes. Due to this method and 3PCLD, the networks classified the images as symmetrical, symmetrical in one axis, and asymmetrical. Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksConfusion matrixThe networks were tested 5 times on each pair of training, validation and testing sets. The resulting networks are saved for future testing and analysis of the results. For each CNN parameters such as accuracy, true positive rate were defined and calculated according to:Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksResultsThe results were analyzed in three ways: 1. T1 - networks tested on a subset of original images; 2. T8 - networks tested on the original set and its seven copies; 3. IDA - networks tested on the original set and its seven copies but in the worst-case scenario, i.e. if one of the 8 copies of the images has been recognized as asymmetric, all its copies have been classified as asymmetrical. Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksResultsThe classifications results for the asymmetry. The chosen confusion matrix factors true positive ratefor full asymmetry (TPR0), true positive rate for symmetry in one axis (TPR1), true positive rate for full symmetry (TPR2), false positive rate for fullasymmetry (FPR0), false positive rate for symmetry in one axis (FPR1), false positive rate for full asymmetry full symmetry (FPR2) with theiraverage (AVG), variance (VAR), minimum (MIN) and maximum (MAX) values for the chosen CNN network. Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksClassification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksResultsThe classifications results for the asymmetry. The chosen confusion matrix factorsweighted accuracy (w.ACC) with their average (AVG), variance (VAR), minimum (Min) and maximum (Max) values and weighted F1 score (w.F1), weighted Matthewscorrelation coeffi-cient (w.MCC) for the chosen CNN network. Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksConclusionsFrom our research we have chosen the best CNN networks: • VGG19 - true positive rate for the asymmetry 84.62%, weighted accuracy 68.29%, F1 score 0.682 and Matthews correlation coefficient 0.581; • Xception - true positive rate for the asymmetry 92.31%, weighted accuracy 67.41%, F1 score 0.646 and Matthews correlation coefficient 0.533; • Inception-ResNet-v2 - true positive rate for the asymmetry 53.85%, weighted accu-racy 51.57%, F1 score 0.528 and Matthews correlation coefficient 0.295. Our original approach using the defined Invariant Dataset Augmentation shows that the classification characteristics like accuracy and true positive rate as well as the F1 and MCC tests can be much higher (5-20%) than using only original images. Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksŁódź, 22.06.2021Thank you for your attention!Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural NetworksMichał Beczkowski, Norbert Borowski, Piotr Milczarski

Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural Networks

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Classification of Dermatological Asymmetry of the Skin Lesions using Pretrained Convolutional Neural Networks

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