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Automated skin lesion analysis for simultaneous detection and recognition isstill challenging for inter-class homogeneity and intra-class heterogeneity,leading to low generic capability of a Single Convolutional Neural Network(CNN) with limited datasets. This article proposes an end-to-end deep CNN-basedframework for simultaneous detection and recognition of the skin lesions, namedDermo-DOCTOR, consisting of two encoders. The feature maps from two encodersare fused channel-wise, called Fused Feature Map (FFM). The FFM is utilized fordecoding in the detection sub-network, concatenating each stage of twoencoders' outputs with corresponding decoder layers to retrieve the lostspatial information due to pooling in the encoders. For the recognitionsub-network, the outputs of three fully connected layers, utilizing featuremaps of two encoders and FFM, are aggregated to obtain a final lesion class. Wetrain and evaluate the proposed Dermo-Doctor utilizing two publicly availablebenchmark datasets, such as ISIC-2016 and ISIC-2017. The achieved segmentationresults exhibit mean intersection over unions of 85.0 % and 80.0 % respectivelyfor ISIC-2016 and ISIC-2017 test datasets. The proposed Dermo-DOCTOR alsodemonstrates praiseworthy success in lesion recognition, providing the areasunder the receiver operating characteristic curves of 0.98 and 0.91respectively for those two datasets. The experimental results show that theproposed Dermo-DOCTOR outperforms the alternative methods mentioned in theliterature, designed for skin lesion detection and recognition. As theDermo-DOCTOR provides better-results on two different test datasets, even withlimited training data, it can be an auspicious computer-aided assistive toolfor dermatologists.

Dermo-DOCTOR: A framework for concurrent skin lesion detection and  recognition using a deep convolutional neural network with end-to-end dual  encoders