Abstract
Skin Lesion detection and classification are very critical in diagnosing skin malignancy. Existing Deep learning-based Computer-aided diagnosis (CAD) methods still perform poorly on challenging skin lesions with complex features such as fuzzy boundaries, artifacts presence, low contrast with the background and, limited training datasets. They also rely heavily on a suitable turning of millions of parameters which often leads to over-fitting, poor generalization, and heavy consumption of computing resources. This study proposes a new framework that performs both segmentation and classification of skin lesions for automated detection of skin cancer. The proposed framework consists of two stages: the first stage leverages on an encoder-decoder Fully Convolutional Network (FCN) to learn the complex and inhomogeneous skin lesion features with the encoder stage learning the coarse appearance and the decoder learning the lesion borders details. Our FCN is designed with the sub-networks connected through a series of skip pathways that incorporate long skip and short-cut connections unlike, the only long skip connections commonly used in the traditional FCN, for residual learning strategy and effective training. The network also integrates the Conditional Random Field (CRF) module which employs a linear combination of Gaussian kernels for its pairwise edge potentials for contour refinement and lesion boundaries localization. The second stage proposes a novel FCN-based DenseNet framework that is composed of dense blocks that are merged and connected via the concatenation strategy and transition layer. The system also employs hyper-parameters optimization techniques to reduce network complexity and improve computing efficiency. This approach encourages feature reuse and thus requires a small number of parameters and effective with limited data. The proposed model was evaluated on publicly available HAM10000 dataset of over 10000 images consisting of 7 different categories of diseases with 98% accuracy, 98.5% recall, and 99% of AUC score respectively.
Highlights
A Malignant tumor is a disorder in the human body in which unusual cells divide uncontrollably and destroy body tissue [1]
This research examines three major factors that limit the performance of deep learning techniques in the analysis of skin lesion images: Firstly, the performance of deep learning methods is reliant on the appropriate tuning of a large number of parameters
The methodology consists of two main components; the first component is an encoder-decoder network integrated with a fully connected Conditional Random Field (CRF) for lesion contour and boundaries refinement to produce highly accurate, soft segmentation maps; the second component is an Fully Convolutional Network (FCN)-based Densenet framework composing of six consecutive dense blocks with a fixed feature maps size connected with a transition layer for effective classification process
Summary
A Malignant tumor is a disorder in the human body in which unusual cells divide uncontrollably and destroy body tissue [1]. These traditional approaches to skin lesions detection are highly intensive and laborious. The performance of deep learning methods is primarily leveraged on large labeled datasets [32] to hierarchically learn the features that correspond to the appearance and the semantics of the skin lesion images [31] They generally require large training data set to build efficient models and utilizing limited labeled data in a situation with skin cancer analysis can result in over-fitting and poor generalization [31]. Training deep learning methods with limited data can lead to the generation of the coarse region of interest (ROI) detections and poor boundary definitions [30]
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