International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 07 | July 2022
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
Skin Disease Detection using Convolutional Neural Network Srujan S A1, Chirag M Shetty2 , Mohammed Adil3 ,Sarang P K4 , Roopitha C H5 1,2,3,4Students,
Department of CS&E, MITE, Karnataka, India Professor, Department of CS&E, MITE, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - Dermatology remains one of the foremost 2. LITERATURE REVIEW 5Assist.
branches of science that is uncertain and complicated because of the sheer number of diseases that affect the skin and the uncertainty surrounding their diagnosis. The variation in these diseases can be seen because of many environmental, geographical, and gene factors and also the human skin is considered one of the most uncertain and troublesome terrains particularly due to the presence of hair, its deviations in tone and other similar mitigating factors. Skin disease diagnosis at present includes a series of pathological laboratory tests for the identification of the correct disease and among them, cancers of the skin are some of the worst. Skin cancers can prove to be fatal, particularly if not treated at the initial stage. The Convolutional Neural Network system proposed in this paper aims at identifying seven skin cancers: Melanocytic Nevi, Melanoma, Benign keratosis-like lesions, Basal cell carcinoma, Actinic keratoses, Vascular lesions, and Dermatofibroma. The dataset used is "Skin Cancer MNSIT: HAM10000" and was obtained from Kaggle. It has a disproportionate number of images for each disease class, some have well over a thousand while others have a few hundreds.
[1] Classification of skin cancer using CNN analysis of Raman Spectra. The performance of convolutional neural networks is compared and also the projection on latent structures with discriminant analysis for discriminating carcinoma using the analysis of Raman spectra with a high autofluorescence background stimulated by a 785 nm laser. They’ve registered the spectra of 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 epithelial cell carcinomas and 413 benign tumors) in vivo with a conveyable Raman setup and created classification models both for convolutional neural networks and projection on latent structures approaches. To test the classification model’s stability, a 10-fold crossvalidation was performed for all created models. To avoid model overfitting, the info was divided into a training set (80% of spectral dataset) and a test set (20% of spectral dataset). The results for various classification tasks demonstrate that the convolutional neural networks significantly (p<0.01) outperforms the projection on latent structures.
Key Words: Dermatology, Skin Disease, Cancer,
[2] Channel Attention based Convolutional Network for skin disease classification.
Convolutional Neural Network, MNSIT: HAM10000
1. INTRODUCTION
This research aims to develop a system for detecting skin diseases employing a Convolution Neural Network (CNN). The proposed model named Eff2Net is constructed on EfficientNetV2 in conjunction with the Efficient Channel Attention (ECA) block. This research attempts to switch the quality Squeeze and Excitation (SE) block within the EfficientNetV2 architecture with the ECA block. By doing so, it had been observed that there was a big call for the full number of trainable parameters. The proposed CNN learnt around 16 M parameters to classify the disease, which is relatively less than the present deep learning approaches reported within the literature. This disease classification was performed on four classes: acne, keratosis (AK), melanoma, and psoriasis.
Skin Cancers have wreaked havoc since the early ages and it is particularly because of the sheer number of cancers that are present that they pose such a high risk - It is difficult to diagnose them without a laboratory test. In our attempt to bring about a change in this ecosystem, we have proposed an automatic skin cancer classification system that can help people in identifying the particular type of pigmented lesion that has taken over their skin. The idea behind this project is to make it possible for a common man to get a sense of the disease affecting his/her skin so they can get a head start in preparing for its betterment and also the doctor in charge can get an idea about the type of cancer, which ultimately helps in faster and efficient diagnosis. We make use of a Convolutional Neural Network that uses Batch Normalization to normalize the layer’s inputs and also makes use of an Adam optimizer. The dataset used is open source obtained from Kaggle and of all the lesions in the dataset, more than 50% has been confirmed through histopathology.
© 2022, IRJET
|
Impact Factor value: 7.529
[3] The Automated skin lesion segmentation using attention based deep Convolutional Neural Network To advance the digital process of segmentation, a deep learning-based end-to-end framework is proposed for automatic dermoscopic image segmentation. The
|
ISO 9001:2008 Certified Journal
|
Page 2162