The skin is the most significant organ of the human figure, as it accommodates all body elements, such as bones and tissues. Nearly 1.79% of the global diseases are skin diseases [1]. Skin diseases can be germs, cancerous, or provocative, affecting different kinds of people, such as children, young and older adults [2]. Many people suffer from skin problems, which eventually lead to skin cancer. Skin diseases are the fourth driving reason for nonfatal illness problems on the planet, and the three most common diseases in the world [3]. Skin diseases caused substantial economic burdens in high and low-income countries. Skin disease can affect all parts of life, such as personal relationships, workplace, social environment, physical movement, and emotional well-being of each other. Suicidal attempts are more in patients having skin diseases [4]. However, making an automated diagnostic system can improve the early detection of this disease. For automated diagnosis, many inventions in the medical image processing field, particularly magnetic resonance imaging (MRI), computed tomography (CT) scan, and digital subtraction angiography (DSA), which determined high-resolution features, was produced for automated diagnosis [5]. Similarly, many public datasets on skin diseases are available for research, and International Skin Imaging Collaboration (ISIC) and Mendeley are used for the proposed study.

Skin cancer is the most well-known type of cancer. Skin cancer can be an abnormal injury of cells. There are four skin cancer classes: Actinic Keratosis, Basal cell carcinoma, Squamous cell carcinoma, and Melanoma. Early detection of cancer assists in treating it effectively. Cancer spread to other body organs due to late diagnosis, can no longer be treated [6]. According to research in the USA, at least one person dies from Melanoma every hour. Also, every year, 9730 patients lose their lives, and 87,110 Melanoma cases were reported [7]. Furthermore, in 2016, there were 6800 Melanoma patients in Canada, where 1150 patients died [8].

SK disease occurs due to benign epidermal cells based on delayed maturation. Melanocytic nevus (MN) and Basal cell carcinoma (BCC) have more profound injuries, which can be differentiated accurately and adequately [9]. BCC is the most common skin cancer and it mainly affects aged people. Epidemiological studies showed that the proportion of BCC is rising every year, and the new generation rate shows a continuously higher trend [10]. Moreover, there is no noticeable difference between Actinic keratosis (AK) and Squamous cell carcinoma (SCC) they are very similar.

Due to the severity of skin infections, several conditions happened in a patient's life like impairment of daily actions, unemployment, loss of confidence, disturbance, suicidal attempt, loss of internal organs, and death in deadly skin cancers such as melanoma, etc. If the detection of these diseases is delayed or incorrect, it may lead to treatment delays, no treatment, or even improper treatment. In the literature, several machines and deep learning algorithms are used to segregate target classes [11,12]. To reduce the morbidity rate, cost, and mortality, skin diseases should be treated in the early stages to overcome these consequences [13]. All the above-described diseases were misclassified due to the same color or intensity level, and another reason for misclassification is an imbalanced dataset. The proposed framework includes

An improved deep learning-based Lesion semantic segmentation is proposed.

The rest of the article is divided into Related Work-2, Proposed Methodology-3, Results, and Discussion-4, and Conclusion-5.

This part summarizes the most advanced and past work in which we can see the skin disease classification at single, binary, or multi-level using different features. With the help of ABCD rule implementation, an automated system is developed for the most common skin cancer, melanoma, which identifies either it is malignant or benign. After implementing the ABCD rule, dermatologists confirm the phase of melanoma and, according to this phase, proposed possible treatments [14]. A computerized smartphone application is used to detect melanoma skin cancer using the ABCD rule. Image capture capabilities, combined with segmentation and preprocessing, are used to extract ABCD features for each skin lesion image, to identify the disease uniquely [15].

The ABCD rule and texture features are combined to provide an automatic diagnosis of melanoma cancer. In this study, a computational method using dermoscopic images is developed to help dermatologists differentiate between non-melanoma and melanoma skin lesions [16]. A novel approach is introduced to identify melanoma using the ABCD rule based on mobile devices. Different sensor-based mobile devices are used to input images and accurately detect melanoma cancer from input images [17]. A study was conducted to discriminate benign lesions and malignant melanoma using the ABCD rule to separate benign lesions from malignant [18].

Melanoma skin cancer is a dangerous type of cancer that kills more people each year than other types of skin cancer. Based on non-linear and linear features, the proposed system automatically diagnosed melanoma. Different features extracted from melanoma images, such as texture features and ABCD rule, distinguish between malignant melanoma and benign lesion [19]. Effective medication for melanoma is critical at the initial stage. According to all the above studies is about melanoma is the most vulnerable form of skin cancer. As a result, more death occurs every year. Thresholding-based techniques are used for segmentation and statistical feature extraction. GLCM (Gray Level Co-occurrence Matrix), Color, Asymmetry, Diameter, and Border features are extracted from collected images [20]. Based on specific characteristics (Energy, Autocorrelation, Homogeneity, Contrast, Entropy), an automated system is introduced to help physicians or doctors to easily classify or detect skin cancer types (AK, BCC, SCC, M) [21].

Deep learning techniques were used to classify six significant diseases as (AK, rosacea (ROS), BCC, SCC, lupus erythematosus (LE), and SK) [22]. Skin cancer is still a challenging task in deep and machine learning fields. The proposed method classifies two disease classes: melanoma and seborrheic keratosis using deep neural networks [23]. Using a deep neural network with human expertise, this algorithm classifies four common skin diseases: MN, BCC, SK, and psoriasis (P) [24].

The proposed framework steps with CNN-based Segmentation and Fused Features based classification is shown in Fig. 1. First, the dataset is normalized using six classes of skin disease. Data is then fed to CNN Network for Segmentation, which returns the lesion areas, which are later segmented using size filter operation. After that, the Deep features using AlexNet, GLCM features, and ABCD rules are extracted from segmented lesions. These features are later given to SVM for classification.

The ISIC and Mendeley dataset is used that is publicly available. The 4363 images were collected based on six common skin diseases. The targeted six classes are Nevus (N), SK, Melanoma (M), SCC, AK, and BCC.

The melanoma and nevus images gathered from Mendeley [25], and the other four types of diseases (SK, BCC, SCC, AK) images were collected from the ISIC archive [26]. The proposed study will focus on the above-mentioned diseases for two reasons: First, diseases that appear on the face, such as BSS, SCC, M, and N. Second, AK and SK disease commonly transition from benign to malignant due to inappropriate medication.

The input image is the first and essential step to begin the proposed framework. After the collection of images, labels are prepared using ground-truth values by annotating rectangular or irregular-shaped polygons, and it finally generates ground-truth values in the form of image masks. The originally collected and normalized input images of target diseases are presented in Fig. 2.

This study's main goal is to classify multiple skin disease images using a deep convolutional neural network. A total of 4363 skin disease images were collected from (ISIC Archive, Mendeley) based on six types of different widespread diseases. The description of the dataset is mentioned in table Tab. 1.

The collected images are of different sizes, which is incapable of training the CNN model. The resize function is performed on the dataset so that images convert in the same size—the image 256 × 256 × 3 with “zero centers” normalization input size given to the CNN input layer. A total of 3053 images are used to train, and the other 1310 images are used for testing.

This paper discussed the analysis and classification of multi-level skin disease images using a combination of features for accurate detection. Segmentation was performed on images using the CNN to achieve 0.80313% accuracy. After segmentation, the segmented images were obtained to a size filter operation to remove noise. Statistical features are extracted using the PCA and GLCM algorithms, and Deep features are extracted using the AlexNet transfer-learning method. The ABCD rule features are also extracted. These features are concatenated one by one for classification. In terms of sensitivity, specificity, accuracy, and error rate, the SVM algorithm outperforms other methods. The proposed framework achieves 91% sensitivity, 98% specificity, 94% precision, 95.4% accuracy, and an error rate of 0.04, which is better than other compared approaches.

The proposed method achieves higher accuracy using ABCD; statistical and deep features than other methods compared in research work. However, the proposed framework uses 4363 images for six types of skin diseases. The dataset size and number of diseases can be increased, which can affect the performance of the proposed study.