Analysis of Deep Networks with Residual Blocks and Different Activation Functions: Classification of Skin Diseases

Classification of large-scale image database of various skin diseases using deep learning

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02440-y ◽

2021 ◽

Author(s):

Masaya Tanaka ◽

Atsushi Saito ◽

Kosuke Shido ◽

Yasuhiro Fujisawa ◽

Kenshi Yamasaki ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Skin Diseases ◽

Image Database

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Classification of skin diseases based on improved MobileNetV2

10.1109/ccdc52312.2021.9602387 ◽

2021 ◽

Author(s):

Yu Jia Cheng ◽

Wei Lin ◽

Yuan Zhen Liu ◽

Lu Sun

Keyword(s):

Skin Diseases

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An Analysis of State-of-the-art Activation Functions For Supervised Deep Neural Network

10.31219/osf.io/2zk6a ◽

2021 ◽

Author(s):

Anh Nguyen ◽

Khoa Pham ◽

Dat Ngo ◽

Thanh Ngo ◽

Lam Pham

Keyword(s):

Neural Network ◽

Supervised Classification ◽

Deep Neural Network ◽

State Of The Art ◽

Network Architectures ◽

Activation Functions ◽

Scene Classification ◽

Learning Network ◽

Deep Learning Network

This paper provides an analysis of state-of-the-art activation functions with respect to supervised classification of deep neural network. These activation functions comprise of Rectified Linear Units (ReLU), Exponential Linear Unit (ELU), Scaled Exponential Linear Unit (SELU), Gaussian Error Linear Unit (GELU), and the Inverse Square Root Linear Unit (ISRLU). To evaluate, experiments over two deep learning network architectures integrating these activation functions are conducted. The first model, basing on Multilayer Perceptron (MLP), is evaluated with MNIST dataset to perform these activation functions.Meanwhile, the second model, likely VGGish-based architecture, is applied for Acoustic Scene Classification (ASC) Task 1A in DCASE 2018 challenge, thus evaluate whether these activation functions work well in different datasets as well as different network architectures.

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African Skin: Different Types, Needs and Diseases

International Journal of TROPICAL DISEASE & Health ◽

10.9734/ijtdh/2019/v36i330143 ◽

2019 ◽

pp. 1-13

Author(s):

Olumayowa Abimbola Oninla ◽

Samuel Olorunyomi Oninla ◽

Bolaji Ibiesa Otike-Odibi ◽

Mufutau Muphy Oripelaye ◽

Fatai Olatunde Olanrewaju ◽

...

Keyword(s):

Skin Color ◽

Skin Diseases ◽

Skin Pigmentation ◽

Geographical Location ◽

Skin Care ◽

Skin Type ◽

Protective Properties ◽

Tropical Regions ◽

The Right

Microscopic structures in the skin are basically the same in all races. Differences are found in histology and physiology of the skin resulting in different skin types, needs and prevailing skin diseases. Skin pigmentation (with the photo-protective properties), and the barrier function of the stratum corneum are the main differences between African and Caucasian skin. The geographic distribution of UV radiation (UVR) has a positive correlation with geographical location. The darker-skinned populations are closer to the equator where there are high amounts of UVR especially in the tropical regions of the world. African skin has the greatest variability in skin color. Africa has both white and dark skinned individuals with the darker-skinned populations being mostly around the equator. Leslie Baumann introduced four parameters that more accurately characterized skin types than previous classification of dry, oily, normal and combination skin. These are dry or oily – D/O; sensitive or resistant – S/R; pigmented or non-pigmented – P/N, and wrinkled or unwrinkled skin – W/T. Combinations of these further produced sixteen skin phenotypes. Dark skinned individuals often have the PT types while the light skinned mostly have the NW types. Skin needs basically depends on the type. Identifying the skin type is fundamental to providing the right skin care. According to Baumann, the fundamental elements of skin care are mild cleansing, hydrating (moisturization with humectants and emollients), replenishing (with lipids, ceramides and fatty acids) and skin protection (UV protection and increased humidity). Skin diseases are associated with skin type. Eczema is more typical in people with DS combinations while acne is associated with OS skin type (especially OSNT and OSPT). Prevalence of skin diseases varies within African communities from 35% to 87% with skin infections affecting 22-46% and eczemas 13-21% of patients in various studies.

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