  Automated skin lesion analysis for simultaneous detection and recognition is
still 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-based
framework for simultaneous detection and recognition of the skin lesions, named
Dermo-DOCTOR, consisting of two encoders. The feature maps from two encoders
are fused channel-wise, called Fused Feature Map (FFM). The FFM is utilized for
decoding in the detection sub-network, concatenating each stage of two
encoders' outputs with corresponding decoder layers to retrieve the lost
spatial information due to pooling in the encoders. For the recognition
sub-network, the outputs of three fully connected layers, utilizing feature
maps of two encoders and FFM, are aggregated to obtain a final lesion class. We
train and evaluate the proposed Dermo-Doctor utilizing two publicly available
benchmark datasets, such as ISIC-2016 and ISIC-2017. The achieved segmentation
results exhibit mean intersection over unions of 85.0 % and 80.0 % respectively
for ISIC-2016 and ISIC-2017 test datasets. The proposed Dermo-DOCTOR also
demonstrates praiseworthy success in lesion recognition, providing the areas
under the receiver operating characteristic curves of 0.98 and 0.91
respectively for those two datasets. The experimental results show that the
proposed Dermo-DOCTOR outperforms the alternative methods mentioned in the
literature, designed for skin lesion detection and recognition. As the
Dermo-DOCTOR provides better-results on two different test datasets, even with
limited training data, it can be an auspicious computer-aided assistive tool
for dermatologists.
