A Novel Framework for Melanoma Lesion Segmentation Using Multiparallel Depthwise Separable and Dilated Convolutions with Swish Activations

Abstract

Skin cancer remains one of the deadliest kinds of cancer, with a survival rate of about 18-20%. Early diagnosis and segmentation of the most lethal kind of cancer, melanoma, is a challenging and critical task. To diagnose medicinal conditions of melanoma lesions, different researchers proposed automatic and traditional approaches to accurately segment the lesions. However, visual similarity among lesions and intraclass differences are very high, which leads to low-performance accuracy. Furthermore, traditional segmentation algorithms often require human inputs and cannot be utilized in automated systems. To address all of these issues, we provide an improved segmentation model based on depthwise separable convolutions that act on each spatial dimension of the image to segment the lesions. The fundamental idea behind these convolutions is to divide the feature learning steps into two simpler parts that are spatial learning of features and a step for channel combination. Besides this, we employ parallel multidilated filters to encode multiple parallel features and broaden the view of filters with dilations. Moreover, for performance evaluation, the proposed approach is evaluated on three different datasets including DermIS, DermQuest, and ISIC2016. The finding indicates that the suggested segmentation model has achieved the Dice score of 97% for DermIS and DermQuest and 94.7% for the ISBI2016 dataset, respectively.

Figure 1

A schematic overview of the proposed methodology.

Figure 2

Image enhancement on ISBI2016 dataset; row 1 depicts the original dataset images, row 2 depicts the images after closing morphological operation, and row 3 depicts results after sharpening.

Figure 3

Architecture of proposed segmentation model.

Figure 4

Depthwise separable convolutions.

Figure 5

Results of augmentation on DermQuest dataset.

Figure 6

Results of melanoma segmentation on DermIS dataset.

Figure 7

Results of melanoma segmentation DermQuest dataset.

Figure 8

Sample melanoma segmentation results of ISIC2016 dataset from the skin with the respective masks and contour images.

Figure 9

Comparison in terms of accuracy, Jaccard, and Dice scores with challenge winners.

Figure 10

Segmentation performance of each test image in the ISBI2016 dataset.

Figure 11

Loss and accuracy graphs of each dataset during training.

Figure 12

Results of channel activation of intermediate layers of the model.