Abstract
Studies on the detection of early stage melanoma have recently gained significant interest. Computer aided diagnosis systems based on neural networks, machine learning, convolutional neural networks (CNNs), and deep learning help early stage detection considerably. The colour and shapes of the images created by the pixels are crucial for the CNNs, as the pixels and associated pictures are interrelated just as a person’s fingerprint is unique. By observing this relationship, the pixel values of each picture with its neighborhoods were determined by a fuzzy logic-based system and a unique fingerprint matrix named Fuzzy Correlation Map (FCov-Map) was produced. The fuzzy logic system has four inputs and one output. The advantage of CNNs trained with fuzzy covariance maps is to eliminate both the limited availability of medical grade training data and the need for extensive image preprocessing. The fuzzy logic output is fed to the pretrained AlexNet CNN algorithm. To deliver a reliable result, a deep CNN needs a large amount of data to process. However, to obtain and use the required sufficient data for diseases is not cost- and time-effective. Therefore, the suggested fuzzy logic-based fuzzy correlation map is tackling this issue to solve the limitedness of training CNN data set.
Highlights
Cancer is a common word used to describe a range of related diseases
The images of the lesion area are given to AlexNet as inputs, whereas the system we developed uses the output of the fuzzy correlation map as inputs to the AlexNet
Due to the developments in machine learning, convolutional neural networks (CNNs), and deep learning, innovative new methods have been developed leading to their integration with conventional methods already present
Summary
Cancer is a common word used to describe a range of related diseases. In its all forms and types, some associated cells begin to divide uncontrollably and penetrate into the surrounding tissues leading to cancer. Digital image processing of melanoma lesions without biopsies is an effective way to detect skin cancer at an early stage. To reach this end, feature extraction is proved as an essential tool to analyse the image. CNN architecture, using deep learning, has achieved its success via the use of three main tools: segmentation, object detection, and classification This success is primarily attributed to the capability of high-level of semantic meaning extraction from image feature models [14]. New melanoma detection methods are needed; a hybrid architecture combining different techniques to recognise and segment the dermoscopic images is available [16]
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